diff --git "a/v3_nexus.ipynb" "b/v3_nexus.ipynb"
new file mode 100644--- /dev/null
+++ "b/v3_nexus.ipynb"
@@ -0,0 +1,2510 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b5323165",
+   "metadata": {},
+   "source": [
+    "# v3 — Nexus on Env v2 (Local SFT + Rubric-Aware GRPO + Frozen-LoRA Opponents)\n",
+    "\n",
+    "Trains Qwen 2.5 7B (LoRA) as Nexus on env v2 (composable rubrics + hidden objectives).\n",
+    "\n",
+    "**Pipeline:**\n",
+    "1. **Local SFT** on `sft_data_mixed_balanced.jsonl` (200 pairs, all 5 countries × all 13 actions) — ~7 min\n",
+    "2. **GRPO** on env v2 with stratified hidden objectives, frozen-LoRA opponents — ~4 hr × 40 steps\n",
+    "3. **Plots + push** to HuggingFace\n",
+    "\n",
+    "**Key v3 changes from v2:**\n",
+    "- Env v2 reward (5 composable rubrics + hidden objectives)\n",
+    "- Trajectory reward: `0.5×dense + 0.5×final` (no `rank_bonus` — env v2 hidden rubric provides natural variance)\n",
+    "- v3 hyperparams: temperature **1.0**, β **0.01**, grad clip **10.0**, 40 steps\n",
+    "- Stratified hidden objectives — Nexus cycles through all 8 secrets\n",
+    "- Frozen-LoRA opponents (Option B) — other 4 countries use a frozen SFT adapter\n",
+    "- KL reference is SFT (not base Qwen)\n",
+    "- Per-rubric component logging → 5 learning curves\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b191e09",
+   "metadata": {},
+   "source": [
+    "## Cell 1 — GPU check"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6530526d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name, memory.total [MiB], memory.free [MiB]\n",
+      "NVIDIA L4, 23034 MiB, 22564 MiB\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c103fb9f",
+   "metadata": {},
+   "source": [
+    "## Cell 2 — Install dependencies (~3 min)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b242ce76",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ deps installed\n"
+     ]
+    }
+   ],
+   "source": [
+    "import subprocess, sys\n",
+    "\n",
+    "PKGS = [\n",
+    "    \"torch\", \"transformers\", \"datasets\", \"accelerate\", \"peft\", \"trl\",\n",
+    "    \"bitsandbytes\", \"matplotlib\", \"huggingface_hub\", \"pydantic\", \"openai\",\n",
+    "    \"safetensors\",\n",
+    "]\n",
+    "subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"--upgrade\", \"pip\"], check=True)\n",
+    "subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"unsloth\", \"unsloth_zoo\"], check=True)\n",
+    "subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", *PKGS], check=True)\n",
+    "print(\"✅ deps installed\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12d3e71e",
+   "metadata": {},
+   "source": [
+    "## Cell 3 — Suppress warnings + verify torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "21a6b3b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch=2.10.0+cu128  cuda=True  device=NVIDIA L4\n",
+      "VRAM total: 23.7 GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "import warnings, logging\n",
+    "import transformers\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "transformers.logging.set_verbosity_error()\n",
+    "logging.getLogger(\"transformers\").setLevel(logging.ERROR)\n",
+    "\n",
+    "import torch\n",
+    "print(f\"torch={torch.__version__}  cuda={torch.cuda.is_available()}  device={torch.cuda.get_device_name(0)}\")\n",
+    "print(f\"VRAM total: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b07689cf",
+   "metadata": {},
+   "source": [
+    "## Cell 4 — Clone geopolicy repo + verify env v2\n",
+    "\n",
+    "Pulls env v2 code from GitHub. Confirms it has the new rubric system + hidden objectives."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8e3c30e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Cloning into 'geopolicy'...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ env v2 verified — 8 objectives, 5 rubrics, task3 hidden weight = 0.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, subprocess, sys\n",
+    "\n",
+    "GEOPOLICY_REPO = \"https://github.com/adityakgp/geopolicy.git\"  # update if your repo URL differs\n",
+    "LOCAL_REPO = \"geopolicy\"\n",
+    "\n",
+    "if not os.path.isdir(LOCAL_REPO):\n",
+    "    subprocess.run([\"git\", \"clone\", GEOPOLICY_REPO, LOCAL_REPO], check=True)\n",
+    "else:\n",
+    "    subprocess.run([\"git\", \"-C\", LOCAL_REPO, \"pull\"], check=True)\n",
+    "\n",
+    "subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\",\n",
+    "                \"-r\", f\"{LOCAL_REPO}/requirements.txt\"], check=False)\n",
+    "sys.path.insert(0, os.path.abspath(LOCAL_REPO))\n",
+    "\n",
+    "# Verify env v2 features\n",
+    "from server.scoring import TaskRubric, TASK_WEIGHTS\n",
+    "from server.rubrics import (EconomicRubric, DiplomaticRubric, MilitaryRubric,\n",
+    "                             StabilityRubric, HiddenObjectiveRubric)\n",
+    "from config.objectives import OBJECTIVES, all_objective_ids\n",
+    "\n",
+    "assert len(OBJECTIVES) == 8, \"expected 8 hidden objectives in env v2\"\n",
+    "assert \"hidden\" in TASK_WEIGHTS[\"task3\"], \"expected hidden weight in task3\"\n",
+    "print(f\"✅ env v2 verified — 8 objectives, 5 rubrics, task3 hidden weight = {TASK_WEIGHTS['task3']['hidden']}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ce6463c",
+   "metadata": {},
+   "source": [
+    "## Cell 5 — Sanity rollout on env v2 (verify hidden_objective + reward_components)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "14c92a21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "focal: aria → secret objective: COALITION_BUILDER (Coalition Builder)\n",
+      "reward: 0.3359\n",
+      "reward_components: {'economic': 0.5780000000000001, 'diplomatic': 0.25, 'military': 0.5, 'stability': 0.635, 'hidden': 0.0}\n",
+      "task_rubric weights: {'economic': 0.2, 'diplomatic': 0.2, 'military': 0.15, 'stability': 0.15, 'hidden': 0.3}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from server.environment import GeoPolicyEnv\n",
+    "from models.action import GeoAction\n",
+    "\n",
+    "env = GeoPolicyEnv()\n",
+    "obs = env.reset(task_id=\"task3\", seed=0)\n",
+    "print(f\"focal: {obs.country_id} → secret objective: {obs.hidden_objective_id} ({obs.hidden_objective_name})\")\n",
+    "\n",
+    "action = GeoAction(action_type=\"WAIT\", source_country=obs.country_id)\n",
+    "out = env.step(action)\n",
+    "print(f\"reward: {out.reward}\")\n",
+    "print(f\"reward_components: {out.metadata['reward_components']}\")\n",
+    "print(f\"task_rubric weights: {env.task_rubric.weights}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c8b06f1",
+   "metadata": {},
+   "source": [
+    "## Cell 6 — Load SFT dataset\n",
+    "\n",
+    "Looks for `sft_data_mixed_balanced.jsonl` (200 pairs, balanced 5 countries × 13 actions).\n",
+    "Cell 4 clones the geopolicy repo, which already contains the dataset at\n",
+    "`geopolicy/final-round/data/`, so this cell finds it automatically. Fallback paths\n",
+    "also work if you uploaded it to the working directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d0ffa27d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ loaded 200 SFT pairs from sft_data_mixed_balanced.jsonl\n",
+      "   keys: ['prompt', 'completion', 'meta']\n",
+      "   sample completion: {\"action_type\":\"NEGOTIATE_PEACE\",\"target_country\":\"verdania\"}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "SFT_DATA_PATH = None\n",
+    "candidates = [\n",
+    "    \"geopolicy/final-round/data/sft_data_mixed_balanced.jsonl\",  # via Cell 4 git clone\n",
+    "    \"final-round/data/sft_data_mixed_balanced.jsonl\",\n",
+    "    \"data/sft_data_mixed_balanced.jsonl\",\n",
+    "    \"sft_data_mixed_balanced.jsonl\",\n",
+    "]\n",
+    "for alt in candidates:\n",
+    "    if os.path.isfile(alt):\n",
+    "        SFT_DATA_PATH = alt\n",
+    "        break\n",
+    "\n",
+    "assert SFT_DATA_PATH, (\n",
+    "    \"Could not find sft_data_mixed_balanced.jsonl. \"\n",
+    "    \"Cell 4 should have cloned the geopolicy repo which contains it; \"\n",
+    "    \"alternatively, upload it to the working directory. Tried: \" + \", \".join(candidates)\n",
+    ")\n",
+    "\n",
+    "with open(SFT_DATA_PATH) as f:\n",
+    "    sft_pairs = [json.loads(line) for line in f]\n",
+    "\n",
+    "print(f\"✅ loaded {len(sft_pairs)} SFT pairs from {SFT_DATA_PATH}\")\n",
+    "print(f\"   keys: {list(sft_pairs[0].keys())}\")\n",
+    "print(f\"   sample completion: {sft_pairs[0]['completion'][:120]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9e51757",
+   "metadata": {},
+   "source": [
+    "## Cell 7 — Load Qwen 7B 4-bit + fresh LoRA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "397e740d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
+      "Unsloth: Your Flash Attention 2 installation seems to be broken. Using Xformers instead. No performance changes will be seen.\n",
+      "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
+      "==((====))==  Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 5.5.0.\n",
+      "   \\\\   /|    NVIDIA L4. Num GPUs = 1. Max memory: 22.034 GB. Platform: Linux.\n",
+      "O^O/ \\_/ \\    Torch: 2.10.0+cu128. CUDA: 8.9. CUDA Toolkit: 12.8. Triton: 3.6.0\n",
+      "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.35. FA2 = False]\n",
+      " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
+      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98a8fb102e9d48bf93d0630a1829b04f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/5.55G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8c887b73ab54decbc61a66b7acf26ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/339 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a56f87f6ee0c4b88be1257d6df7c9985",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/271 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d0c30b61c8c49138044319fb17fb77b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/7.36k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0891ae8005154cac99c6a4db6455a622",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d38762747464c80af37eb0c1b2da520",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "merges.txt:   0%|          | 0.00/1.67M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c53fcb9f97c844058ac0822c4f916e5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7356dc35b9014a38940427eff1cbd95b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "added_tokens.json:   0%|          | 0.00/605 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85e53bcf8a134e5ba146ff66b55eebc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "unsloth/Qwen2.5-7B-Instruct-bnb-4bit does not have a padding token! Will use pad_token = <|PAD_TOKEN|>.\n",
+      "✅ Qwen 7B + fresh LoRA loaded (5.8 GB VRAM)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from unsloth import FastLanguageModel\n",
+    "\n",
+    "MAX_SEQ_LENGTH = 2560\n",
+    "BASE_MODEL = \"unsloth/Qwen2.5-7B-Instruct-bnb-4bit\"\n",
+    "\n",
+    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
+    "    model_name=BASE_MODEL,\n",
+    "    max_seq_length=MAX_SEQ_LENGTH,\n",
+    "    load_in_4bit=True,\n",
+    "    dtype=None,\n",
+    ")\n",
+    "model = FastLanguageModel.get_peft_model(\n",
+    "    model,\n",
+    "    r=16,\n",
+    "    target_modules=[\"q_proj\",\"k_proj\",\"v_proj\",\"o_proj\",\"gate_proj\",\"up_proj\",\"down_proj\"],\n",
+    "    lora_alpha=32,\n",
+    "    lora_dropout=0,\n",
+    "    bias=\"none\",\n",
+    "    use_gradient_checkpointing=\"unsloth\",\n",
+    "    random_state=42,\n",
+    "    max_seq_length=MAX_SEQ_LENGTH,\n",
+    ")\n",
+    "print(f\"✅ Qwen 7B + fresh LoRA loaded ({torch.cuda.memory_allocated(0)/1e9:.1f} GB VRAM)\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75724ef8",
+   "metadata": {},
+   "source": [
+    "## Cell 8 — System prompt + format SFT data for chat training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0cf9ec76",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1d58087d24c49eabb96b8c7f106013e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/200 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Dataset formatted: 200 examples, columns=['text']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import Dataset\n",
+    "\n",
+    "if tokenizer.pad_token is None:\n",
+    "    tokenizer.pad_token = tokenizer.eos_token\n",
+    "tokenizer.padding_side = \"left\"\n",
+    "\n",
+    "SYSTEM_PROMPT = '''You are a strategic advisor for a country in a geopolitical simulation.\n",
+    "You receive your country's state and must choose ONE action for this turn.\n",
+    "\n",
+    "Some episodes give your country a SECRET OBJECTIVE in addition to the public goal.\n",
+    "Other countries do NOT know your secret objective. Pursue it without revealing it openly.\n",
+    "\n",
+    "RESPOND WITH ONLY A VALID JSON OBJECT. No explanation, no markdown, no extra text.\n",
+    "\n",
+    "AVAILABLE ACTIONS:\n",
+    "- WAIT: Recover +3 all resources, +5 stability. Cost: 0\n",
+    "- DEVELOP: Grow a resource. Fields: resource. Cost: 20 economy\n",
+    "- TRADE: Exchange. Fields: target_country, resource, amount, counter_resource, counter_amount. Cost: 5\n",
+    "- PROPOSE_ALLIANCE: Defense pact. Fields: target_country. Cost: 0\n",
+    "- BREAK_ALLIANCE: Exit alliance. Fields: target_country. Cost: 0\n",
+    "- INVADE: Attack. Fields: target_country, amount (0.1-1.0 force). Cost: 30\n",
+    "- DEFEND: Boost defense this turn. Cost: 0\n",
+    "- SANCTION: Economic pressure. Fields: target_country. Cost: 10\n",
+    "- THREATEN: Demand tribute. Fields: target_country. Cost: 5\n",
+    "- NEGOTIATE_PEACE: End war. Fields: target_country. Cost: 10\n",
+    "- SPY: Reveal exact resources. Fields: target_country. Cost: 25\n",
+    "- COUNTER_INTEL: Block spies for 3 turns. Cost: 15\n",
+    "- USE_SPECIAL: Unique ability. Fields: target_country. Cost: 20\n",
+    "\n",
+    "JSON FORMAT:\n",
+    "{\"action_type\":\"...\",\"target_country\":\"...\",\"resource\":\"...\",\"amount\":..,\"counter_resource\":\"...\",\"counter_amount\":..}'''\n",
+    "\n",
+    "\n",
+    "def format_pair(ex):\n",
+    "    messages = [\n",
+    "        {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
+    "        {\"role\": \"user\", \"content\": ex[\"prompt\"]},\n",
+    "        {\"role\": \"assistant\", \"content\": ex[\"completion\"]},\n",
+    "    ]\n",
+    "    return {\"text\": tokenizer.apply_chat_template(messages, tokenize=False)}\n",
+    "\n",
+    "\n",
+    "dataset = Dataset.from_list(sft_pairs).map(\n",
+    "    format_pair, remove_columns=[c for c in [\"prompt\", \"completion\", \"meta\"] if c in sft_pairs[0]]\n",
+    ")\n",
+    "print(f\"✅ Dataset formatted: {len(dataset)} examples, columns={dataset.column_names}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fca284cf",
+   "metadata": {},
+   "source": [
+    "## Cell 9 — Checkpoint stats callback (logs loss every 5 steps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cc59f997",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ CheckpointStatsCallback ready\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "from transformers import TrainerCallback\n",
+    "\n",
+    "\n",
+    "class CheckpointStatsCallback(TrainerCallback):\n",
+    "    def __init__(self, mode=\"sft\", train_log_path=\"train_log_sft_v3.jsonl\"):\n",
+    "        self.mode = mode\n",
+    "        self.train_log_path = train_log_path\n",
+    "        self._train_start = None\n",
+    "        self._history = []\n",
+    "\n",
+    "    def on_train_begin(self, args, state, control, **kwargs):\n",
+    "        self._train_start = time.time()\n",
+    "        with open(self.train_log_path, \"w\") as f:\n",
+    "            pass\n",
+    "\n",
+    "    def on_log(self, args, state, control, logs=None, **kwargs):\n",
+    "        if not logs:\n",
+    "            return\n",
+    "        entry = {\"step\": state.global_step, \"epoch\": state.epoch, **logs}\n",
+    "        self._history.append(entry)\n",
+    "        with open(self.train_log_path, \"a\") as f:\n",
+    "            f.write(json.dumps(entry) + \"\\n\")\n",
+    "\n",
+    "    def on_save(self, args, state, control, **kwargs):\n",
+    "        ckpt_dir = os.path.join(args.output_dir, f\"checkpoint-{state.global_step}\")\n",
+    "        if not os.path.isdir(ckpt_dir):\n",
+    "            return\n",
+    "        recent = self._history[-20:]\n",
+    "        loss_vals = [h[\"loss\"] for h in recent if \"loss\" in h]\n",
+    "        loss_mean = sum(loss_vals) / len(loss_vals) if loss_vals else None\n",
+    "        stats = {\n",
+    "            \"step\": state.global_step, \"epoch\": state.epoch,\n",
+    "            \"wall_time_seconds\": time.time() - self._train_start,\n",
+    "            \"mode\": self.mode, \"loss_recent_mean\": loss_mean,\n",
+    "            \"memory_peak_gb\": torch.cuda.max_memory_allocated(0) / 1e9,\n",
+    "        }\n",
+    "        with open(os.path.join(ckpt_dir, \"checkpoint_stats.json\"), \"w\") as f:\n",
+    "            json.dump(stats, f, indent=2, default=str)\n",
+    "        loss_str = f\"{loss_mean:.4f}\" if loss_mean else \"n/a\"\n",
+    "        print(f\"  → checkpoint-{state.global_step} saved (loss_mean={loss_str})\")\n",
+    "\n",
+    "\n",
+    "print(\"✅ CheckpointStatsCallback ready\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba6d6641",
+   "metadata": {},
+   "source": [
+    "## Cell 10 — Run SFT (~7 min, 3 epochs)\n",
+    "\n",
+    "Lessons from v2 SFT findings:\n",
+    "- 3 epochs (not 5 — overfit risk)\n",
+    "- Save every 15 steps for mid-checkpoints\n",
+    "- Pick early-middle checkpoint to avoid overfit (don't pick lowest-loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3e4a8ffe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "033bfa5064b0411abff9e9368bf2a2c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Unsloth: Tokenizing [\"text\"] (num_proc=12):   0%|          | 0/200 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🦥 Unsloth: Padding-free auto-enabled, enabling faster training.\n",
+      "Training 200 examples × 3 epochs (~37 steps)\n",
+      "Output: /data/sft_v3_checkpoints\n",
+      "\n",
+      "Unsloth: Will smartly offload gradients to save VRAM!\n",
+      "{'loss': '2.417', 'grad_norm': '1.056', 'learning_rate': '9.928e-05', 'epoch': '0.4'}\n",
+      "{'loss': '1.669', 'grad_norm': '1.054', 'learning_rate': '9.143e-05', 'epoch': '0.8'}\n",
+      "{'loss': '1.026', 'grad_norm': '1.202', 'learning_rate': '7.622e-05', 'epoch': '1.16'}\n",
+      "  → checkpoint-15 saved (loss_mean=1.7037)\n",
+      "{'loss': '0.5147', 'grad_norm': '0.9177', 'learning_rate': '5.635e-05', 'epoch': '1.56'}\n",
+      "{'loss': '0.2948', 'grad_norm': '0.5147', 'learning_rate': '3.536e-05', 'epoch': '1.96'}\n",
+      "{'loss': '0.2258', 'grad_norm': '0.3897', 'learning_rate': '1.697e-05', 'epoch': '2.32'}\n",
+      "  → checkpoint-30 saved (loss_mean=1.0244)\n",
+      "{'loss': '0.1936', 'grad_norm': '0.3807', 'learning_rate': '4.439e-06', 'epoch': '2.72'}\n",
+      "  → checkpoint-39 saved (loss_mean=0.9057)\n",
+      "{'train_runtime': '515.9', 'train_samples_per_second': '1.163', 'train_steps_per_second': '0.076', 'train_loss': '0.8318', 'epoch': '3'}\n",
+      "\n",
+      "✅ SFT done. Peak VRAM: 8.2 GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "from trl import SFTTrainer, SFTConfig\n",
+    "\n",
+    "SFT_OUTPUT_DIR = \"/data/sft_v3_checkpoints\" if os.path.isdir(\"/data\") else \"./sft_v3_checkpoints\"\n",
+    "SFT_LOG_PATH = \"train_log_sft_v3.jsonl\"\n",
+    "\n",
+    "FastLanguageModel.for_training(model)\n",
+    "\n",
+    "sft_args = SFTConfig(\n",
+    "    output_dir=SFT_OUTPUT_DIR,\n",
+    "    num_train_epochs=3,\n",
+    "    per_device_train_batch_size=4,\n",
+    "    gradient_accumulation_steps=4,        # effective batch 16\n",
+    "    learning_rate=1e-4,\n",
+    "    warmup_ratio=0.05,\n",
+    "    lr_scheduler_type=\"cosine\",\n",
+    "    logging_steps=5,\n",
+    "    save_steps=15,\n",
+    "    save_total_limit=4,\n",
+    "    optim=\"adamw_8bit\",\n",
+    "    weight_decay=0.01,\n",
+    "    bf16=True, fp16=False,\n",
+    "    seed=42,\n",
+    "    max_length=2560,\n",
+    "    dataset_text_field=\"text\",\n",
+    "    report_to=\"none\",\n",
+    "    push_to_hub=False,\n",
+    ")\n",
+    "\n",
+    "stats_cb = CheckpointStatsCallback(mode=\"sft_v3\", train_log_path=SFT_LOG_PATH)\n",
+    "\n",
+    "try:\n",
+    "    trainer = SFTTrainer(model=model, train_dataset=dataset, args=sft_args,\n",
+    "                         processing_class=tokenizer, callbacks=[stats_cb])\n",
+    "except TypeError:\n",
+    "    trainer = SFTTrainer(model=model, train_dataset=dataset, args=sft_args,\n",
+    "                         tokenizer=tokenizer, callbacks=[stats_cb])\n",
+    "\n",
+    "print(f\"Training {len(dataset)} examples × 3 epochs (~{len(dataset)*3//16} steps)\")\n",
+    "print(f\"Output: {SFT_OUTPUT_DIR}\\n\")\n",
+    "torch.cuda.reset_peak_memory_stats(0)\n",
+    "trainer.train()\n",
+    "print(f\"\\n✅ SFT done. Peak VRAM: {torch.cuda.max_memory_allocated(0)/1e9:.1f} GB\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51a08c71",
+   "metadata": {},
+   "source": [
+    "## Cell 11 — Pick best SFT checkpoint\n",
+    "\n",
+    "Lesson from v2 Findings: lowest-loss is overfit. Pick the EARLIEST checkpoint with loss < ~1.5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "51305646",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SFT checkpoints: ['checkpoint-15', 'checkpoint-30', 'checkpoint-39']\n",
+      "  checkpoint-15: loss_mean=1.7036652247111004\n",
+      "  checkpoint-30: loss_mean=1.0243786573410034\n",
+      "  checkpoint-39: loss_mean=0.9056907296180725\n",
+      "\n",
+      "📌 Picked: checkpoint-30\n"
+     ]
+    }
+   ],
+   "source": [
+    "ckpt_dirs = sorted(\n",
+    "    [d for d in os.listdir(SFT_OUTPUT_DIR) if d.startswith(\"checkpoint-\")],\n",
+    "    key=lambda x: int(x.split(\"-\")[1])\n",
+    ")\n",
+    "print(f\"SFT checkpoints: {ckpt_dirs}\")\n",
+    "\n",
+    "ckpt_stats = []\n",
+    "for c in ckpt_dirs:\n",
+    "    sp = os.path.join(SFT_OUTPUT_DIR, c, \"checkpoint_stats.json\")\n",
+    "    if os.path.isfile(sp):\n",
+    "        s = json.load(open(sp))\n",
+    "        ckpt_stats.append((c, s.get(\"loss_recent_mean\", float(\"inf\")) or float(\"inf\")))\n",
+    "        print(f\"  {c}: loss_mean={s.get('loss_recent_mean')}\")\n",
+    "\n",
+    "# Heuristic: pick the middle checkpoint (avoid overfit + avoid undertrained)\n",
+    "SFT_BEST_NAME = ckpt_dirs[len(ckpt_dirs) // 2] if ckpt_dirs else ckpt_dirs[-1]\n",
+    "SFT_BEST_DIR = os.path.join(SFT_OUTPUT_DIR, SFT_BEST_NAME)\n",
+    "print(f\"\\n📌 Picked: {SFT_BEST_NAME}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d5cadaa",
+   "metadata": {},
+   "source": [
+    "## Cell 12 — Reload best SFT weights + add `frozen_opp` adapter\n",
+    "\n",
+    "After this cell:\n",
+    "- Adapter `default` = trained SFT LoRA (will be updated by GRPO)\n",
+    "- Adapter `frozen_opp` = frozen copy of the same weights (used by opponents AND as KL reference)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d54c3cd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ loaded /data/sft_v3_checkpoints/checkpoint-30/adapter_model.safetensors into default adapter (392 tensors)\n",
+      "✅ saved live adapter to /data/sft_v3_frozen\n",
+      "✅ adapters loaded: 'default' (live, 40.37M trainable) + 'frozen_opp' (frozen)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import safetensors.torch\n",
+    "\n",
+    "# 1. Load the chosen SFT weights into the live 'default' adapter\n",
+    "sft_weights_path = os.path.join(SFT_BEST_DIR, \"adapter_model.safetensors\")\n",
+    "state_dict = safetensors.torch.load_file(sft_weights_path)\n",
+    "missing, unexpected = model.load_state_dict(state_dict, strict=False)\n",
+    "print(f\"✅ loaded {sft_weights_path} into default adapter ({len(state_dict)} tensors)\")\n",
+    "\n",
+    "# 2. Save the loaded weights to a tidy path (for the second adapter)\n",
+    "SFT_FROZEN_DIR = \"/data/sft_v3_frozen\" if os.path.isdir(\"/data\") else \"./sft_v3_frozen\"\n",
+    "os.makedirs(SFT_FROZEN_DIR, exist_ok=True)\n",
+    "model.save_pretrained(SFT_FROZEN_DIR)\n",
+    "print(f\"✅ saved live adapter to {SFT_FROZEN_DIR}\")\n",
+    "\n",
+    "# 3. Load the same weights again as a SECOND adapter named 'frozen_opp'\n",
+    "model.load_adapter(SFT_FROZEN_DIR, adapter_name=\"frozen_opp\")\n",
+    "\n",
+    "# 4. Mark all 'frozen_opp' params as non-trainable\n",
+    "for n, p in model.named_parameters():\n",
+    "    if \"frozen_opp\" in n:\n",
+    "        p.requires_grad = False\n",
+    "\n",
+    "# 5. Default to the live adapter for any forward pass\n",
+    "model.set_adapter(\"default\")\n",
+    "\n",
+    "trainable_count = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
+    "print(f\"✅ adapters loaded: 'default' (live, {trainable_count/1e6:.2f}M trainable) + 'frozen_opp' (frozen)\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c115eec2",
+   "metadata": {},
+   "source": [
+    "## Cell 13 — Rollout helpers with per-country adapter switching\n",
+    "\n",
+    "`generate_with_adapter` switches PEFT adapters per country:\n",
+    "- focal (Nexus) → `default` adapter, gradients tracked later in loss\n",
+    "- others → `frozen_opp` adapter, `torch.no_grad()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "39c51db3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ rollout helpers ready (focal=default, opponents=frozen_opp)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json, re\n",
+    "from contextlib import nullcontext\n",
+    "from difflib import get_close_matches\n",
+    "\n",
+    "VALID_COUNTRIES = {\"aria\", \"verdania\", \"ironhold\", \"aqualis\", \"nexus\"}\n",
+    "FOCAL_COUNTRY = \"nexus\"\n",
+    "\n",
+    "\n",
+    "def parse_action(text, country_id):\n",
+    "    text = text.strip()\n",
+    "    action = None\n",
+    "    try:\n",
+    "        action = json.loads(text)\n",
+    "    except Exception:\n",
+    "        m = re.search(r'\\{.*?\\}', text, re.DOTALL)\n",
+    "        if m:\n",
+    "            try: action = json.loads(m.group(0))\n",
+    "            except Exception: pass\n",
+    "    if action is None:\n",
+    "        action = {\"action_type\": \"WAIT\"}\n",
+    "    action[\"source_country\"] = country_id\n",
+    "    target = action.get(\"target_country\")\n",
+    "    if isinstance(target, str):\n",
+    "        t = target.lower().strip()\n",
+    "        if t and t not in VALID_COUNTRIES:\n",
+    "            match = get_close_matches(t, VALID_COUNTRIES, n=1, cutoff=0.5)\n",
+    "            action[\"target_country\"] = match[0] if match else None\n",
+    "    for k in [\"amount\", \"counter_amount\"]:\n",
+    "        v = action.get(k)\n",
+    "        if isinstance(v, str):\n",
+    "            action[k] = float(v) if v.replace('.','').replace('-','').isdigit() else None\n",
+    "    return action\n",
+    "\n",
+    "\n",
+    "def build_prompt(obs_dict, country_id):\n",
+    "    \"\"\"Env-v2-aware prompt builder (includes secret objective for focal).\"\"\"\n",
+    "    lines = [\n",
+    "        f\"COUNTRY: {obs_dict['country_name']} ({obs_dict['country_id']}) -- {obs_dict['archetype']}\",\n",
+    "        f\"TURN: {obs_dict['turn']}/{obs_dict['max_turns']}\",\n",
+    "        f\"OBJECTIVE: {obs_dict['task_objective']}\",\n",
+    "    ]\n",
+    "    if obs_dict.get(\"hidden_objective_id\"):\n",
+    "        lines.append(\"--- YOUR SECRET OBJECTIVE ---\")\n",
+    "        lines.append(f\"{obs_dict['hidden_objective_name']}: {obs_dict['hidden_objective_description']}\")\n",
+    "        lines.append(\"This is HIDDEN from other countries. Pursue it strategically.\")\n",
+    "        lines.append(\"-----------------------------\")\n",
+    "    lines += [\n",
+    "        \"\",\n",
+    "        f\"YOUR RESOURCES: oil={obs_dict['oil']:.0f} water={obs_dict['water']:.0f} food={obs_dict['food']:.0f} mil={obs_dict['military']:.0f} eco={obs_dict['economy']:.0f}\",\n",
+    "        f\"  stab={obs_dict['internal_stability']:.0f}  rep={obs_dict['reputation']:.0f}  NPS={obs_dict['current_nps']:.1f}\",\n",
+    "        f\"ALLIANCES: {obs_dict['alliances'] or 'None'}   AT WAR: {obs_dict['at_war_with'] or 'None'}\",\n",
+    "        f\"SPECIAL: {obs_dict['special_ability']} (cd={obs_dict['special_ability_cooldown']})\",\n",
+    "        \"\",\n",
+    "        \"OTHER COUNTRIES:\",\n",
+    "    ]\n",
+    "    for rid, info in obs_dict.get(\"other_countries\", {}).items():\n",
+    "        if isinstance(info, dict):\n",
+    "            tier = f\"oil={info.get('oil_tier','?')} mil={info.get('military_tier','?')} eco={info.get('economy_tier','?')}\"\n",
+    "            lines.append(f\"  {info.get('country_name', rid)} ({rid}): {tier}\")\n",
+    "    if obs_dict.get(\"active_global_event\"):\n",
+    "        lines.append(f\"\\nACTIVE EVENT: {obs_dict['active_global_event']}\")\n",
+    "    lines.append(\"\\nChoose your action. Respond with ONLY a JSON object.\")\n",
+    "    return \"\\n\".join(lines)\n",
+    "\n",
+    "\n",
+    "def _build_chat(obs_dict, country_id):\n",
+    "    user_msg = build_prompt(obs_dict, country_id)\n",
+    "    return tokenizer.apply_chat_template(\n",
+    "        [{\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
+    "         {\"role\": \"user\", \"content\": user_msg}],\n",
+    "        tokenize=False, add_generation_prompt=True,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def generate_with_adapter(prompts, adapter_name, max_new_tokens=128, temperature=1.0):\n",
+    "    if isinstance(prompts, str):\n",
+    "        prompts = [prompts]\n",
+    "    if not prompts:\n",
+    "        return []\n",
+    "    model.set_adapter(adapter_name)\n",
+    "    inputs = tokenizer(prompts, return_tensors=\"pt\", padding=True, truncation=True,\n",
+    "                       max_length=MAX_SEQ_LENGTH).to(\"cuda\")\n",
+    "    with torch.no_grad():\n",
+    "        outputs = model.generate(\n",
+    "            **inputs, max_new_tokens=max_new_tokens, do_sample=True,\n",
+    "            temperature=temperature, pad_token_id=tokenizer.eos_token_id,\n",
+    "        )\n",
+    "    plen = inputs.input_ids.shape[1]\n",
+    "    return [tokenizer.decode(o[plen:], skip_special_tokens=True) for o in outputs]\n",
+    "\n",
+    "\n",
+    "def generate_actions_for_turn(env, focal=FOCAL_COUNTRY, temperature=1.0):\n",
+    "    prompts = {cid: _build_chat(env.get_observation(cid).model_dump(), cid)\n",
+    "               for cid in env.active_country_ids}\n",
+    "    actions = {}\n",
+    "    if focal in prompts:\n",
+    "        out = generate_with_adapter([prompts[focal]], adapter_name=\"default\",\n",
+    "                                    temperature=temperature)\n",
+    "        actions[focal] = parse_action(out[0], focal)\n",
+    "    others = [cid for cid in env.active_country_ids if cid != focal]\n",
+    "    if others:\n",
+    "        out = generate_with_adapter([prompts[cid] for cid in others],\n",
+    "                                    adapter_name=\"frozen_opp\",\n",
+    "                                    temperature=temperature)\n",
+    "        for cid, c in zip(others, out):\n",
+    "            actions[cid] = parse_action(c, cid)\n",
+    "    model.set_adapter(\"default\")\n",
+    "    return actions\n",
+    "\n",
+    "\n",
+    "print(\"✅ rollout helpers ready (focal=default, opponents=frozen_opp)\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37e1b28b",
+   "metadata": {},
+   "source": [
+    "## Cell 13.5 — SFT sanity check: does the model output valid JSON for env v2?\n",
+    "\n",
+    "Quick check before kicking off GRPO: feed the SFT-trained model an env v2 prompt\n",
+    "(with hidden objective) and verify the output is parseable JSON with a valid action\n",
+    "type and target. Catches issues early before the 4-hour GRPO run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "9d154b75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== SFT sanity check on env v2 (task3) ===\n",
+      "\n",
+      "✅ aria        obj=SABOTEUR            action_type=COUNTER_INTEL         target=verdania\n",
+      "    raw output: {\"action_type\":\"COUNTER_INTEL\",\"target_country\":\"verdania\"}\n",
+      "✅ verdania    obj=COALITION_BUILDER   action_type=DEFEND                target=None\n",
+      "    raw output: {\"action_type\":\"DEFEND\"}\n",
+      "✅ ironhold    obj=LONE_WOLF           action_type=DEFEND                target=None\n",
+      "    raw output: {\"action_type\":\"DEFEND\"}\n",
+      "✅ aqualis     obj=SPY_MASTER          action_type=SPY                   target=verdania\n",
+      "    raw output: {\"action_type\":\"SPY\",\"target_country\":\"verdania\"}\n",
+      "✅ nexus       obj=TRADE_HEGEMON       action_type=SPY                   target=aria\n",
+      "    raw output: {\"action_type\":\"SPY\",\"target_country\":\"aria\"}\n",
+      "\n",
+      "=== 5/5 valid ===\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def sanity_check_sft_on_env_v2(n_samples=3):\n",
+    "    \"\"\"Generate one action per country and validate JSON / action / target.\"\"\"\n",
+    "    env.reset(task_id=\"task3\", seed=123)\n",
+    "    print(f\"=== SFT sanity check on env v2 (task3) ===\\n\")\n",
+    "\n",
+    "    pass_count = 0\n",
+    "    fail_details = []\n",
+    "\n",
+    "    for cid in env.active_country_ids[:n_samples]:\n",
+    "        obs_dict = env.get_observation(cid).model_dump()\n",
+    "        has_secret = obs_dict.get(\"hidden_objective_id\") is not None\n",
+    "\n",
+    "        prompt = _build_chat(obs_dict, cid)\n",
+    "        outputs = generate_with_adapter([prompt], adapter_name=\"default\",\n",
+    "                                         max_new_tokens=128, temperature=0.7)\n",
+    "        raw = outputs[0].strip()\n",
+    "        action = parse_action(raw, cid)\n",
+    "\n",
+    "        # Validate\n",
+    "        valid_action = action.get(\"action_type\", \"\").upper() in {\n",
+    "            \"WAIT\", \"DEVELOP\", \"TRADE\", \"PROPOSE_ALLIANCE\", \"BREAK_ALLIANCE\",\n",
+    "            \"INVADE\", \"DEFEND\", \"SANCTION\", \"THREATEN\", \"NEGOTIATE_PEACE\",\n",
+    "            \"SPY\", \"COUNTER_INTEL\", \"USE_SPECIAL\",\n",
+    "        }\n",
+    "        target = action.get(\"target_country\")\n",
+    "        valid_target = target is None or target in VALID_COUNTRIES\n",
+    "\n",
+    "        # Try to construct a GeoAction (this is what env actually uses)\n",
+    "        from pydantic import ValidationError\n",
+    "        ga_ok = False\n",
+    "        ga_err = None\n",
+    "        try:\n",
+    "            GeoAction(**action)\n",
+    "            ga_ok = True\n",
+    "        except ValidationError as e:\n",
+    "            ga_err = str(e)[:120]\n",
+    "\n",
+    "        ok = valid_action and valid_target and ga_ok\n",
+    "        pass_count += int(ok)\n",
+    "        status = \"✅\" if ok else \"❌\"\n",
+    "        secret = obs_dict.get(\"hidden_objective_id\", \"(none)\")\n",
+    "        print(f\"{status} {cid:10s}  obj={secret:18s}  action_type={action.get('action_type'):20s}  target={target}\")\n",
+    "        print(f\"    raw output: {raw[:120]}\")\n",
+    "        if not ok:\n",
+    "            fail_details.append((cid, action, raw, ga_err))\n",
+    "\n",
+    "    print(f\"\\n=== {pass_count}/{n_samples} valid ===\")\n",
+    "    if fail_details:\n",
+    "        print(\"\\nFAILURES:\")\n",
+    "        for cid, action, raw, err in fail_details:\n",
+    "            print(f\"  {cid}: parsed={action}, geo_action_err={err}\")\n",
+    "            print(f\"      raw: {raw}\")\n",
+    "\n",
+    "    assert pass_count >= max(1, n_samples - 1), (\n",
+    "        f\"SFT model failing JSON output on env v2 prompts ({pass_count}/{n_samples} passed). \"\n",
+    "        f\"Investigate before launching GRPO. Likely fixes: re-run SFT with more epochs, \"\n",
+    "        f\"or verify SYSTEM_PROMPT matches the chat template.\"\n",
+    "    )\n",
+    "    return pass_count == n_samples\n",
+    "\n",
+    "\n",
+    "sanity_check_sft_on_env_v2(n_samples=5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6721dd79",
+   "metadata": {},
+   "source": [
+    "## Cell 14 — `play_one_rollout_v3` — captures per-rubric components per turn\n",
+    "\n",
+    "Trajectory reward: `0.5 × mean(per_turn_rewards) + 0.5 × env.grade_country()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1667537b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ rollout helper ready — trajectory_reward = 0.5×dense + 0.5×final\n"
+     ]
+    }
+   ],
+   "source": [
+    "DENSE_WEIGHT = 0.5\n",
+    "FINAL_WEIGHT = 0.5\n",
+    "\n",
+    "\n",
+    "def play_one_rollout_v3(env, country_id=FOCAL_COUNTRY, temperature=1.0):\n",
+    "    \"\"\"Run one full episode. Returns (history, trajectory_reward, dense, final, components_traj).\"\"\"\n",
+    "    history = []\n",
+    "    while not env.state.done:\n",
+    "        obs_dict = {cid: env.get_observation(cid).model_dump() for cid in env.active_country_ids}\n",
+    "        action_dicts = generate_actions_for_turn(env, focal=country_id, temperature=temperature)\n",
+    "        actions = {}\n",
+    "        for cid in env.active_country_ids:\n",
+    "            ad = action_dicts.get(cid, {\"action_type\": \"WAIT\"}) or {\"action_type\": \"WAIT\"}\n",
+    "            ad[\"source_country\"] = cid\n",
+    "            try:\n",
+    "                actions[cid] = GeoAction(**ad)\n",
+    "            except Exception:\n",
+    "                actions[cid] = GeoAction(action_type=\"WAIT\", source_country=cid)\n",
+    "        results = env.step_all(actions)\n",
+    "        first = env.active_country_ids[0]\n",
+    "        history.append({\n",
+    "            \"turn\": env.state.current_turn,\n",
+    "            \"obs\": obs_dict,\n",
+    "            \"actions\": {cid: actions[cid].model_dump() for cid in env.active_country_ids},\n",
+    "            \"rewards\": {cid: results[cid].reward for cid in env.active_country_ids},\n",
+    "            \"rankings\": results[first].metadata.get(\"rankings\", []),\n",
+    "            \"results\": {cid: results[cid].metadata.get(\"action_result\", {}) for cid in env.active_country_ids},\n",
+    "            \"components\": {cid: results[cid].metadata.get(\"reward_components\", {}) for cid in env.active_country_ids},\n",
+    "        })\n",
+    "\n",
+    "    turn_rewards = [t[\"rewards\"][country_id] for t in history if country_id in t[\"rewards\"]]\n",
+    "    dense = sum(turn_rewards) / max(len(turn_rewards), 1)\n",
+    "    final = env.grade_country(country_id)\n",
+    "    traj = DENSE_WEIGHT * dense + FINAL_WEIGHT * final\n",
+    "\n",
+    "    rubric_names = [\"economic\", \"diplomatic\", \"military\", \"stability\", \"hidden\"]\n",
+    "    components_traj = {n: [t[\"components\"][country_id].get(n, 0.0) for t in history] for n in rubric_names}\n",
+    "\n",
+    "    return history, traj, dense, final, components_traj\n",
+    "\n",
+    "\n",
+    "print(f\"✅ rollout helper ready — trajectory_reward = {DENSE_WEIGHT}×dense + {FINAL_WEIGHT}×final\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34d5ea99",
+   "metadata": {},
+   "source": [
+    "## Cell 15 — GRPO core: KL + grpo_step_v3\n",
+    "\n",
+    "v3 hyperparams: temperature 1.0, β 0.01, grad clip 10, group=4, 40 steps. KL reference is\n",
+    "`frozen_opp` adapter (= SFT init), not base Qwen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "4f60a674",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ optimizer: 40.37M trainable  lr=1e-05  β=0.01  clip=10.0\n",
+      "✅ grpo_step_v3 ready (KL via frozen_opp adapter, stratified objective injection)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import bitsandbytes as bnb\n",
+    "import numpy as np\n",
+    "from collections import Counter, defaultdict\n",
+    "\n",
+    "# === v3 hyperparams ===\n",
+    "GRPO_LR = 1e-5\n",
+    "GRPO_BETA = 0.01\n",
+    "GRPO_GROUP_SIZE = 4\n",
+    "GRPO_TEMPERATURE = 1.0\n",
+    "GRAD_CLIP_NORM = 10.0\n",
+    "\n",
+    "# Optimizer touches only the 'default' adapter's params (frozen_opp params have requires_grad=False)\n",
+    "trainable_params = [p for p in model.parameters() if p.requires_grad]\n",
+    "grpo_optimizer = bnb.optim.AdamW8bit(trainable_params, lr=GRPO_LR, weight_decay=0.01)\n",
+    "print(f\"✅ optimizer: {sum(p.numel() for p in trainable_params)/1e6:.2f}M trainable  lr={GRPO_LR}  β={GRPO_BETA}  clip={GRAD_CLIP_NORM}\")\n",
+    "\n",
+    "\n",
+    "def compute_logprobs_and_kl(prompt_str, completion_str):\n",
+    "    \"\"\"Returns (mean_cur_logp_with_grad, mean_kl, n_tokens). k3 KL estimator.\n",
+    "\n",
+    "    π_θ = 'default' adapter (live)\n",
+    "    π_ref = 'frozen_opp' adapter (SFT init, not base Qwen)\n",
+    "    \"\"\"\n",
+    "    full = prompt_str + completion_str\n",
+    "    full_ids = tokenizer(full, return_tensors=\"pt\", truncation=True,\n",
+    "                        max_length=MAX_SEQ_LENGTH).input_ids.to(\"cuda\")\n",
+    "    prompt_ids = tokenizer(prompt_str, return_tensors=\"pt\", truncation=True,\n",
+    "                           max_length=MAX_SEQ_LENGTH).input_ids.to(\"cuda\")\n",
+    "    plen = prompt_ids.shape[1]\n",
+    "    if full_ids.shape[1] <= plen:\n",
+    "        z = torch.tensor(0.0, requires_grad=True, device=\"cuda\")\n",
+    "        return z, z.detach(), 0\n",
+    "\n",
+    "    targets = full_ids[:, plen:]\n",
+    "    n_tokens = int(targets.shape[1])\n",
+    "\n",
+    "    # Current policy: default adapter, gradients flow\n",
+    "    model.set_adapter(\"default\")\n",
+    "    out_cur = model(full_ids)\n",
+    "    cur_lp_dist = torch.nn.functional.log_softmax(out_cur.logits[:, plen-1:-1, :], dim=-1)\n",
+    "    cur_token_lp = cur_lp_dist.gather(2, targets.unsqueeze(-1)).squeeze(-1)\n",
+    "\n",
+    "    # Reference policy: frozen_opp adapter (SFT-frozen), no grad\n",
+    "    with torch.no_grad():\n",
+    "        model.set_adapter(\"frozen_opp\")\n",
+    "        out_ref = model(full_ids)\n",
+    "        ref_lp_dist = torch.nn.functional.log_softmax(out_ref.logits[:, plen-1:-1, :], dim=-1)\n",
+    "        ref_token_lp = ref_lp_dist.gather(2, targets.unsqueeze(-1)).squeeze(-1)\n",
+    "        model.set_adapter(\"default\")\n",
+    "\n",
+    "    # k3 KL estimator: log_ratio = log(π_ref / π_θ); KL = exp(log_r) - log_r - 1\n",
+    "    log_ratio = ref_token_lp.detach() - cur_token_lp\n",
+    "    k3_kl = torch.exp(log_ratio) - log_ratio - 1.0\n",
+    "\n",
+    "    return cur_token_lp.mean(), k3_kl.mean(), n_tokens\n",
+    "\n",
+    "\n",
+    "def grpo_step_v3(step_idx, scheduled_objective, group_size=GRPO_GROUP_SIZE,\n",
+    "                 temperature=GRPO_TEMPERATURE, beta=GRPO_BETA):\n",
+    "    \"\"\"One v3 GRPO step. Stratified hidden objective for the focal country.\"\"\"\n",
+    "    FastLanguageModel.for_inference(model)\n",
+    "    env.reset(task_id=\"task3\", seed=step_idx)\n",
+    "    env.countries[FOCAL_COUNTRY].hidden_objective = scheduled_objective\n",
+    "    snap = env.snapshot()\n",
+    "\n",
+    "    rollouts = []\n",
+    "    for g in range(group_size):\n",
+    "        env.restore(snap)\n",
+    "        h, traj, dense, final_g, comps = play_one_rollout_v3(env, FOCAL_COUNTRY, temperature=temperature)\n",
+    "        rollouts.append({\"history\": h, \"reward\": traj, \"dense\": dense, \"final\": final_g,\n",
+    "                         \"components\": comps, \"n_turns\": len(h)})\n",
+    "\n",
+    "    rewards = [r[\"reward\"] for r in rollouts]\n",
+    "    mean_r = float(np.mean(rewards))\n",
+    "    std_r = float(np.std(rewards)) + 1e-8\n",
+    "    advantages = [(r - mean_r) / std_r for r in rewards]\n",
+    "\n",
+    "    rubric_names = [\"economic\", \"diplomatic\", \"military\", \"stability\", \"hidden\"]\n",
+    "    rollout_rubric_means = {n: [float(np.mean(r[\"components\"][n])) if r[\"components\"][n] else 0.0\n",
+    "                                 for r in rollouts] for n in rubric_names}\n",
+    "\n",
+    "    action_mix = Counter()\n",
+    "    parse_failures = 0\n",
+    "    for ro in rollouts:\n",
+    "        for t in ro[\"history\"]:\n",
+    "            atype = t[\"actions\"][FOCAL_COUNTRY][\"action_type\"]\n",
+    "            action_mix[atype] += 1\n",
+    "            if t[\"results\"].get(FOCAL_COUNTRY, {}).get(\"fallback\"):\n",
+    "                parse_failures += 1\n",
+    "\n",
+    "    FastLanguageModel.for_training(model)\n",
+    "    grpo_optimizer.zero_grad()\n",
+    "    losses, pg_terms, kl_terms = [], [], []\n",
+    "    completion_tokens_total = 0\n",
+    "    sample_completion = None\n",
+    "\n",
+    "    for ro_idx, (ro, adv) in enumerate(zip(rollouts, advantages)):\n",
+    "        for turn_idx, turn in enumerate(ro[\"history\"]):\n",
+    "            prompt = _build_chat(turn[\"obs\"][FOCAL_COUNTRY], FOCAL_COUNTRY)\n",
+    "            ad = {k: v for k, v in turn[\"actions\"][FOCAL_COUNTRY].items() if k != \"source_country\"}\n",
+    "            completion = json.dumps(ad, separators=(\",\", \":\"))\n",
+    "\n",
+    "            cur_lp_mean, kl_mean, n_tok = compute_logprobs_and_kl(prompt, completion)\n",
+    "            pg_loss = -(cur_lp_mean * adv)\n",
+    "            kl_loss = beta * kl_mean\n",
+    "            loss = pg_loss + kl_loss\n",
+    "            loss.backward()\n",
+    "\n",
+    "            losses.append(loss.item())\n",
+    "            pg_terms.append(pg_loss.item())\n",
+    "            kl_terms.append(kl_loss.item())\n",
+    "            completion_tokens_total += n_tok\n",
+    "            if sample_completion is None:\n",
+    "                sample_completion = {\n",
+    "                    \"rollout_id\": ro_idx, \"turn\": turn_idx, \"advantage\": adv,\n",
+    "                    \"prompt_preview\": prompt[-300:], \"completion\": completion,\n",
+    "                    \"completion_tokens\": n_tok, \"rollout_reward\": ro[\"reward\"],\n",
+    "                }\n",
+    "\n",
+    "    grad_norm_before = float(torch.nn.utils.clip_grad_norm_(trainable_params, max_norm=float(\"inf\")))\n",
+    "    grad_norm_after = float(torch.nn.utils.clip_grad_norm_(trainable_params, max_norm=GRAD_CLIP_NORM))\n",
+    "    grpo_optimizer.step()\n",
+    "\n",
+    "    return {\n",
+    "        \"step\": step_idx,\n",
+    "        \"scheduled_objective\": scheduled_objective,\n",
+    "        \"mean_reward\": mean_r, \"std_reward\": std_r,\n",
+    "        \"rewards\": rewards, \"advantages\": advantages,\n",
+    "        \"dense_mean\": float(np.mean([r[\"dense\"] for r in rollouts])),\n",
+    "        \"final_mean\": float(np.mean([r[\"final\"] for r in rollouts])),\n",
+    "        \"rubric_components_mean\": {n: float(np.mean(rollout_rubric_means[n])) for n in rubric_names},\n",
+    "        \"rubric_components_std\":  {n: float(np.std(rollout_rubric_means[n]))  for n in rubric_names},\n",
+    "        \"loss_mean\": float(np.mean(losses)) if losses else 0.0,\n",
+    "        \"pg_mean\": float(np.mean(pg_terms)) if pg_terms else 0.0,\n",
+    "        \"kl_mean\": float(np.mean(kl_terms)) if kl_terms else 0.0,\n",
+    "        \"grad_norm_before_clip\": grad_norm_before,\n",
+    "        \"grad_norm_after_clip\": grad_norm_after,\n",
+    "        \"n_examples\": len(losses),\n",
+    "        \"completion_tokens_total\": completion_tokens_total,\n",
+    "        \"action_mix\": dict(action_mix),\n",
+    "        \"distinct_actions\": len(action_mix),\n",
+    "        \"parse_failures\": parse_failures,\n",
+    "        \"vram_peak_gb\": torch.cuda.max_memory_allocated(0) / 1e9,\n",
+    "        \"_rollouts\": rollouts,\n",
+    "        \"_sample_completion\": sample_completion,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "print(\"✅ grpo_step_v3 ready (KL via frozen_opp adapter, stratified objective injection)\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0323f80f",
+   "metadata": {},
+   "source": [
+    "## Cell 16 — Build stratified hidden-objective schedule\n",
+    "\n",
+    "40 steps × 8 objectives = each objective seen ~5 times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "5f95a4de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "objective schedule across 50 steps:\n",
+      "  COALITION_BUILDER    6\n",
+      "  KINGMAKER            7\n",
+      "  LONE_WOLF            6\n",
+      "  PEACEKEEPER          6\n",
+      "  SABOTEUR             7\n",
+      "  SPY_MASTER           6\n",
+      "  SURVIVOR             6\n",
+      "  TRADE_HEGEMON        6\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random as _rnd\n",
+    "from config.objectives import all_objective_ids\n",
+    "\n",
+    "N_STEPS = 50\n",
+    "\n",
+    "OBJECTIVE_POOL = all_objective_ids()  # 8 ids\n",
+    "schedule = (OBJECTIVE_POOL * (N_STEPS // 8 + 1))[:N_STEPS]\n",
+    "_rnd.seed(42)\n",
+    "_rnd.shuffle(schedule)\n",
+    "\n",
+    "from collections import Counter as _C\n",
+    "print(f\"objective schedule across {N_STEPS} steps:\")\n",
+    "for k, v in sorted(_C(schedule).items()):\n",
+    "    print(f\"  {k:20s} {v}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9faa4cf8",
+   "metadata": {},
+   "source": [
+    "## Cell 17 — GRPO smoke test (5 steps, ~25 min)\n",
+    "\n",
+    "Sanity-check that grad norms are sane, KL is non-zero, group_std stays alive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a14735fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time, datetime\n",
+    "\n",
+    "SMOKE_DIR = \"/data/grpo_runs/v3_smoke\" if os.path.isdir(\"/data\") else \"./grpo_runs/v3_smoke\"\n",
+    "os.makedirs(SMOKE_DIR, exist_ok=True)\n",
+    "SMOKE_LOG = os.path.join(SMOKE_DIR, \"train_log.jsonl\")\n",
+    "open(SMOKE_LOG, \"w\").close()\n",
+    "\n",
+    "print(f\"=== v3 SMOKE — 5 steps ===\")\n",
+    "t0 = time.time()\n",
+    "for step in range(1, 6):\n",
+    "    s = grpo_step_v3(step, scheduled_objective=schedule[step - 1])\n",
+    "    s.pop(\"_rollouts\", None); s.pop(\"_sample_completion\", None)\n",
+    "    with open(SMOKE_LOG, \"a\") as f:\n",
+    "        f.write(json.dumps(s, default=str) + \"\\n\")\n",
+    "    print(f\"step {step}: obj={s['scheduled_objective']:18s} reward={s['mean_reward']:.3f} ± {s['std_reward']:.3f}  \"\n",
+    "          f\"kl={s['kl_mean']:.4f}  grad={s['grad_norm_before_clip']:.1f}  actions={s['distinct_actions']}  \"\n",
+    "          f\"vram={s['vram_peak_gb']:.1f}GB\")\n",
+    "print(f\"\\n=== smoke done in {(time.time()-t0)/60:.1f} min ===\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2175770",
+   "metadata": {},
+   "source": [
+    "## Cell 18 — Full GRPO run (50 steps, ~4 hr)\n",
+    "\n",
+    "Logs per-step row to `train_log.jsonl`, dumps full rollouts every 5 steps,\n",
+    "saves checkpoints every 10 steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "664bace2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== v3 FULL — Nexus, 50 steps, env v2, frozen-LoRA opponents ===\n",
+      "  output: /data/grpo_runs/v3\n",
+      "\n",
+      "Unsloth: Will smartly offload gradients to save VRAM!\n",
+      "step   1/50: obj=SABOTEUR           r=0.376 ± 0.010  kl=0.0000  grad=28.2  acts=8  hid=0.24  step_t=354s ETA=289m\n",
+      "step   2/50: obj=PEACEKEEPER        r=0.392 ± 0.058  kl=0.0001  grad=28.1  acts=2  hid=0.00  step_t=328s ETA=273m\n",
+      "step   3/50: obj=TRADE_HEGEMON      r=0.282 ± 0.159  kl=0.0003  grad=27.9  acts=5  hid=0.00  step_t=298s ETA=256m\n",
+      "step   4/50: obj=TRADE_HEGEMON      r=0.234 ± 0.109  kl=0.0003  grad=24.8  acts=5  hid=0.00  step_t=295s ETA=244m\n",
+      "step   5/50: obj=COALITION_BUILDER  r=0.528 ± 0.176  kl=0.0002  grad=25.6  acts=4  hid=0.59  step_t=323s ETA=240m\n",
+      "step   6/50: obj=LONE_WOLF          r=0.429 ± 0.073  kl=0.0002  grad=46.5  acts=6  hid=0.26  step_t=284s ETA=230m\n",
+      "step   7/50: obj=SURVIVOR           r=0.667 ± 0.027  kl=0.0002  grad=14.3  acts=2  hid=1.00  step_t=293s ETA=223m\n",
+      "step   8/50: obj=SABOTEUR           r=0.406 ± 0.017  kl=0.0003  grad=27.7  acts=6  hid=0.33  step_t=327s ETA=219m\n",
+      "step   9/50: obj=LONE_WOLF          r=0.418 ± 0.067  kl=0.0003  grad=25.0  acts=6  hid=0.26  step_t=318s ETA=214m\n",
+      "step  10/50: obj=KINGMAKER          r=0.601 ± 0.082  kl=0.0004  grad=61.8  acts=7  hid=0.64  step_t=267s ETA=206m\n",
+      "  💾 checkpoint-10  avg(last 10) = 0.433\n",
+      "step  11/50: obj=PEACEKEEPER        r=0.400 ± 0.043  kl=0.0002  grad=39.7  acts=2  hid=0.00  step_t=300s ETA=200m\n",
+      "step  12/50: obj=LONE_WOLF          r=0.389 ± 0.089  kl=0.0003  grad=48.2  acts=6  hid=0.27  step_t=343s ETA=197m\n",
+      "step  13/50: obj=COALITION_BUILDER  r=0.808 ± 0.014  kl=0.0003  grad=17.7  acts=2  hid=0.93  step_t=376s ETA=195m\n",
+      "step  14/50: obj=TRADE_HEGEMON      r=0.308 ± 0.108  kl=0.0005  grad=24.2  acts=4  hid=0.00  step_t=319s ETA=190m\n",
+      "step  15/50: obj=PEACEKEEPER        r=0.505 ± 0.022  kl=0.0009  grad=24.9  acts=3  hid=0.00  step_t=304s ETA=184m\n",
+      "step  16/50: obj=SPY_MASTER         r=0.450 ± 0.112  kl=0.0006  grad=20.4  acts=1  hid=0.27  step_t=352s ETA=180m\n",
+      "step  17/50: obj=SURVIVOR           r=0.678 ± 0.027  kl=0.0010  grad=31.1  acts=4  hid=1.00  step_t=362s ETA=176m\n",
+      "step  18/50: obj=KINGMAKER          r=0.367 ± 0.087  kl=0.0023  grad=40.4  acts=5  hid=0.14  step_t=313s ETA=171m\n",
+      "step  19/50: obj=TRADE_HEGEMON      r=0.289 ± 0.086  kl=0.0033  grad=55.6  acts=3  hid=0.00  step_t=349s ETA=166m\n",
+      "step  20/50: obj=KINGMAKER          r=0.446 ± 0.195  kl=0.0023  grad=88.5  acts=8  hid=0.38  step_t=351s ETA=161m\n",
+      "  💾 checkpoint-20  avg(last 10) = 0.464\n",
+      "step  21/50: obj=TRADE_HEGEMON      r=0.179 ± 0.097  kl=0.0014  grad=22.0  acts=3  hid=0.00  step_t=308s ETA=156m\n",
+      "step  22/50: obj=SURVIVOR           r=0.708 ± 0.042  kl=0.0031  grad=42.7  acts=4  hid=1.00  step_t=341s ETA=151m\n",
+      "step  23/50: obj=SABOTEUR           r=0.322 ± 0.130  kl=0.0007  grad=24.4  acts=4  hid=0.33  step_t=332s ETA=146m\n",
+      "step  24/50: obj=KINGMAKER          r=0.684 ± 0.115  kl=0.0011  grad=39.0  acts=7  hid=0.82  step_t=324s ETA=140m\n",
+      "step  25/50: obj=SABOTEUR           r=0.419 ± 0.028  kl=0.0006  grad=26.5  acts=5  hid=0.24  step_t=352s ETA=135m\n",
+      "step  26/50: obj=SPY_MASTER         r=0.567 ± 0.189  kl=0.0007  grad=29.8  acts=1  hid=0.79  step_t=301s ETA=129m\n",
+      "step  27/50: obj=COALITION_BUILDER  r=0.830 ± 0.007  kl=0.0010  grad=29.9  acts=3  hid=0.97  step_t=375s ETA=125m\n",
+      "step  28/50: obj=COALITION_BUILDER  r=0.819 ± 0.002  kl=0.0010  grad=27.8  acts=3  hid=0.96  step_t=382s ETA=120m\n",
+      "step  29/50: obj=SPY_MASTER         r=0.508 ± 0.151  kl=0.0006  grad=14.0  acts=1  hid=0.68  step_t=345s ETA=115m\n",
+      "step  30/50: obj=SURVIVOR           r=0.693 ± 0.011  kl=0.0063  grad=51.4  acts=3  hid=1.00  step_t=349s ETA=110m\n",
+      "  💾 checkpoint-30  avg(last 10) = 0.573\n",
+      "step  31/50: obj=SPY_MASTER         r=0.476 ± 0.124  kl=0.0004  grad=40.3  acts=1  hid=0.40  step_t=306s ETA=104m\n",
+      "step  32/50: obj=COALITION_BUILDER  r=0.818 ± 0.021  kl=0.0009  grad=33.4  acts=6  hid=0.96  step_t=390s ETA=99m\n",
+      "step  33/50: obj=KINGMAKER          r=0.630 ± 0.036  kl=0.0006  grad=35.2  acts=11  hid=0.38  step_t=354s ETA=94m\n",
+      "step  34/50: obj=COALITION_BUILDER  r=0.821 ± 0.008  kl=0.0008  grad=34.5  acts=4  hid=0.96  step_t=356s ETA=88m\n",
+      "step  35/50: obj=LONE_WOLF          r=0.282 ± 0.102  kl=0.0009  grad=24.7  acts=6  hid=0.02  step_t=378s ETA=83m\n",
+      "step  36/50: obj=SABOTEUR           r=0.339 ± 0.100  kl=0.0028  grad=36.9  acts=4  hid=0.18  step_t=347s ETA=78m\n",
+      "step  37/50: obj=PEACEKEEPER        r=0.537 ± 0.002  kl=0.0007  grad=26.0  acts=4  hid=0.00  step_t=373s ETA=72m\n",
+      "step  38/50: obj=SURVIVOR           r=0.605 ± 0.191  kl=0.0019  grad=46.6  acts=3  hid=0.94  step_t=275s ETA=67m\n",
+      "step  39/50: obj=TRADE_HEGEMON      r=0.331 ± 0.106  kl=0.0122  grad=93.3  acts=1  hid=0.00  step_t=291s ETA=61m\n",
+      "step  40/50: obj=LONE_WOLF          r=0.385 ± 0.062  kl=0.0046  grad=46.4  acts=8  hid=0.07  step_t=359s ETA=55m\n",
+      "  💾 checkpoint-40  avg(last 10) = 0.523\n",
+      "step  41/50: obj=LONE_WOLF          r=0.202 ± 0.082  kl=0.0027  grad=48.6  acts=9  hid=0.06  step_t=366s ETA=50m\n",
+      "step  42/50: obj=SURVIVOR           r=0.725 ± 0.021  kl=0.0010  grad=31.4  acts=5  hid=1.00  step_t=348s ETA=44m\n",
+      "step  43/50: obj=SPY_MASTER         r=0.355 ± 0.210  kl=0.0013  grad=18.2  acts=1  hid=0.75  step_t=344s ETA=39m\n",
+      "step  44/50: obj=KINGMAKER          r=0.494 ± 0.042  kl=0.0007  grad=47.6  acts=10  hid=0.20  step_t=357s ETA=33m\n",
+      "step  45/50: obj=SPY_MASTER         r=0.389 ± 0.226  kl=0.0004  grad=78.2  acts=1  hid=0.53  step_t=245s ETA=28m\n",
+      "step  46/50: obj=PEACEKEEPER        r=0.519 ± 0.023  kl=0.0012  grad=31.9  acts=11  hid=0.00  step_t=298s ETA=22m\n",
+      "step  47/50: obj=SABOTEUR           r=0.386 ± 0.104  kl=0.0005  grad=23.3  acts=3  hid=0.46  step_t=287s ETA=17m\n",
+      "step  48/50: obj=SABOTEUR           r=0.372 ± 0.117  kl=0.0007  grad=18.7  acts=3  hid=0.45  step_t=346s ETA=11m\n",
+      "step  49/50: obj=PEACEKEEPER        r=0.543 ± 0.004  kl=0.0016  grad=21.4  acts=9  hid=0.00  step_t=337s ETA=6m\n",
+      "step  50/50: obj=KINGMAKER          r=0.509 ± 0.187  kl=0.0013  grad=41.0  acts=11  hid=0.53  step_t=394s ETA=0m\n",
+      "  💾 checkpoint-50  avg(last 10) = 0.449\n",
+      "\n",
+      "=== v3 COMPLETE in 276.9 min ===\n",
+      "best step 27: reward=0.830, obj=COALITION_BUILDER\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys, time, datetime\n",
+    "\n",
+    "RUN_DIR = \"/data/grpo_runs/v3\" if os.path.isdir(\"/data\") else \"./grpo_runs/v3\"\n",
+    "CHECKPOINTS_DIR = f\"{RUN_DIR}/checkpoints\"\n",
+    "ROLLOUTS_DIR = f\"{RUN_DIR}/rollouts\"\n",
+    "os.makedirs(CHECKPOINTS_DIR, exist_ok=True)\n",
+    "os.makedirs(ROLLOUTS_DIR, exist_ok=True)\n",
+    "\n",
+    "TRAIN_LOG    = f\"{RUN_DIR}/train_log.jsonl\"\n",
+    "SAMPLES_LOG  = f\"{RUN_DIR}/sample_completions.jsonl\"\n",
+    "PER_COUNTRY  = f\"{RUN_DIR}/per_country.jsonl\"\n",
+    "RAW_LOG      = f\"{RUN_DIR}/raw_log.txt\"\n",
+    "for p in [TRAIN_LOG, SAMPLES_LOG, PER_COUNTRY, RAW_LOG]:\n",
+    "    open(p, \"w\").close()\n",
+    "\n",
+    "with open(f\"{RUN_DIR}/run_config.json\", \"w\") as f:\n",
+    "    json.dump({\n",
+    "        \"version\": \"v3\", \"started_at\": datetime.datetime.now().isoformat(),\n",
+    "        \"focal\": FOCAL_COUNTRY, \"n_steps\": N_STEPS, \"group_size\": GRPO_GROUP_SIZE,\n",
+    "        \"lr\": GRPO_LR, \"beta\": GRPO_BETA, \"temperature\": GRPO_TEMPERATURE,\n",
+    "        \"grad_clip_norm\": GRAD_CLIP_NORM,\n",
+    "        \"dense_weight\": DENSE_WEIGHT, \"final_weight\": FINAL_WEIGHT,\n",
+    "        \"sft_init_local\": SFT_BEST_DIR, \"base_model\": BASE_MODEL,\n",
+    "        \"objective_schedule\": schedule, \"stratified_axis\": \"hidden_objective\",\n",
+    "        \"opponent_policy\": \"frozen_lora (Option B)\",\n",
+    "    }, f, indent=2)\n",
+    "\n",
+    "\n",
+    "class _Tee:\n",
+    "    def __init__(self, *streams): self.streams = streams\n",
+    "    def write(self, m):\n",
+    "        for s in self.streams:\n",
+    "            try: s.write(m); s.flush()\n",
+    "            except: pass\n",
+    "    def flush(self):\n",
+    "        for s in self.streams:\n",
+    "            try: s.flush()\n",
+    "            except: pass\n",
+    "\n",
+    "raw = open(RAW_LOG, \"a\")\n",
+    "_orig = sys.stdout\n",
+    "sys.stdout = _Tee(_orig, raw)\n",
+    "\n",
+    "print(f\"=== v3 FULL — Nexus, {N_STEPS} steps, env v2, frozen-LoRA opponents ===\")\n",
+    "print(f\"  output: {RUN_DIR}\\n\")\n",
+    "\n",
+    "full_stats = []\n",
+    "torch.cuda.reset_peak_memory_stats(0)\n",
+    "t_start = time.time()\n",
+    "\n",
+    "try:\n",
+    "    for step in range(1, N_STEPS + 1):\n",
+    "        t0 = time.time()\n",
+    "        s = grpo_step_v3(step, scheduled_objective=schedule[step - 1])\n",
+    "        s[\"elapsed_seconds\"] = time.time() - t0\n",
+    "        s[\"run_version\"] = \"v3\"\n",
+    "        rollouts_full = s.pop(\"_rollouts\")\n",
+    "        sample = s.pop(\"_sample_completion\")\n",
+    "        full_stats.append(s)\n",
+    "\n",
+    "        with open(TRAIN_LOG, \"a\") as f: f.write(json.dumps(s, default=str) + \"\\n\")\n",
+    "        if sample:\n",
+    "            sample[\"step\"] = step\n",
+    "            with open(SAMPLES_LOG, \"a\") as f: f.write(json.dumps(sample, default=str) + \"\\n\")\n",
+    "        with open(PER_COUNTRY, \"a\") as f:\n",
+    "            for ro_idx, ro in enumerate(rollouts_full):\n",
+    "                final_turn = ro[\"history\"][-1]\n",
+    "                f.write(json.dumps({\n",
+    "                    \"step\": step, \"rollout_id\": ro_idx, \"objective\": s[\"scheduled_objective\"],\n",
+    "                    \"rollout_reward_focal\": ro[\"reward\"], \"n_turns\": ro[\"n_turns\"],\n",
+    "                    \"final_rankings\": final_turn.get(\"rankings\", []),\n",
+    "                    \"final_per_country_rewards\": final_turn.get(\"rewards\", {}),\n",
+    "                }, default=str) + \"\\n\")\n",
+    "\n",
+    "        if step % 5 == 0:\n",
+    "            with open(f\"{ROLLOUTS_DIR}/step_{step:03d}.jsonl\", \"w\") as f:\n",
+    "                for ro_idx, ro in enumerate(rollouts_full):\n",
+    "                    f.write(json.dumps({\n",
+    "                        \"step\": step, \"rollout_id\": ro_idx, \"objective\": s[\"scheduled_objective\"],\n",
+    "                        \"focal_reward\": ro[\"reward\"], \"n_turns\": ro[\"n_turns\"],\n",
+    "                        \"history\": ro[\"history\"],\n",
+    "                    }, default=str) + \"\\n\")\n",
+    "\n",
+    "        elapsed_min = (time.time() - t_start) / 60\n",
+    "        eta_min = elapsed_min / step * (N_STEPS - step)\n",
+    "        comps = s[\"rubric_components_mean\"]\n",
+    "        print(f\"step {step:3d}/{N_STEPS}: obj={s['scheduled_objective']:18s} \"\n",
+    "              f\"r={s['mean_reward']:.3f} ± {s['std_reward']:.3f}  kl={s['kl_mean']:.4f}  \"\n",
+    "              f\"grad={s['grad_norm_before_clip']:.1f}  acts={s['distinct_actions']}  \"\n",
+    "              f\"hid={comps['hidden']:.2f}  step_t={s['elapsed_seconds']:.0f}s ETA={eta_min:.0f}m\")\n",
+    "\n",
+    "        if step % 10 == 0:\n",
+    "            ckpt = f\"{CHECKPOINTS_DIR}/checkpoint-{step}\"\n",
+    "            model.save_pretrained(ckpt)\n",
+    "            tokenizer.save_pretrained(ckpt)\n",
+    "            with open(f\"{ckpt}/checkpoint_stats.json\", \"w\") as f:\n",
+    "                json.dump(s, f, indent=2, default=str)\n",
+    "            recent = [x[\"mean_reward\"] for x in full_stats[-10:]]\n",
+    "            print(f\"  💾 checkpoint-{step}  avg(last 10) = {sum(recent)/len(recent):.3f}\")\n",
+    "\n",
+    "    final_dir = f\"{CHECKPOINTS_DIR}/final\"\n",
+    "    model.save_pretrained(final_dir)\n",
+    "    tokenizer.save_pretrained(final_dir)\n",
+    "    best = max(full_stats, key=lambda x: x[\"mean_reward\"])\n",
+    "    print(f\"\\n=== v3 COMPLETE in {(time.time()-t_start)/60:.1f} min ===\")\n",
+    "    print(f\"best step {best['step']}: reward={best['mean_reward']:.3f}, obj={best['scheduled_objective']}\")\n",
+    "finally:\n",
+    "    sys.stdout = _orig\n",
+    "    raw.close()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82ea6192-f588-4d78-8c86-1ca2b66bfc1c",
+   "metadata": {},
+   "source": [
+    "## Cell 18.5 — Auto-pick best checkpoint by `avg(last 10)`\n",
+    "\n",
+    "Cheap-and-fast checkpoint selection — no extra inference. Reads `train_log.jsonl`,\n",
+    "computes the rolling 10-step mean reward at each checkpoint save point\n",
+    "(steps 10, 20, 30, 40, 50), and copies the highest-scoring one to\n",
+    "`checkpoints/best/`. ~1 second wall clock.\n",
+    "\n",
+    "Mid-training checkpoints often beat the final one in RL. This finds that\n",
+    "automatically without burning compute on a full eval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a17b3008-b94b-4ad1-836e-72f9dbb445aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 6 candidates: ['checkpoint-10', 'checkpoint-20', 'checkpoint-30', 'checkpoint-40', 'checkpoint-50', 'final']\n",
+      "\n",
+      "CHECKPOINT          STEP   avg(last 10)   window\n",
+      "-------------------------------------------------------\n",
+      "checkpoint-10         10          0.433     10/10\n",
+      "checkpoint-20         20          0.464     10/10\n",
+      "checkpoint-30         30          0.573     10/10\n",
+      "checkpoint-40         40          0.523     10/10\n",
+      "checkpoint-50         50          0.449     10/10\n",
+      "final                 50          0.449     10/10\n",
+      "\n",
+      "🏆 BEST CHECKPOINT: checkpoint-30\n",
+      "   step: 30\n",
+      "   avg(last 10): 0.573\n",
+      "\n",
+      "💾 copied checkpoint-30 → /data/grpo_runs/v3/checkpoints/best\n",
+      "💾 wrote /data/grpo_runs/v3/best_checkpoint.json\n",
+      "   live 'default' adapter is now using checkpoint-30 weights\n"
+     ]
+    }
+   ],
+   "source": [
+    "import shutil\n",
+    "import safetensors.torch\n",
+    "\n",
+    "# Discover saved checkpoints\n",
+    "candidate_ckpts = sorted(\n",
+    "    [d for d in os.listdir(CHECKPOINTS_DIR)\n",
+    "     if d.startswith(\"checkpoint-\") or d == \"final\"],\n",
+    "    key=lambda x: 999999 if x == \"final\" else int(x.split(\"-\")[1])\n",
+    ")\n",
+    "print(f\"Found {len(candidate_ckpts)} candidates: {candidate_ckpts}\")\n",
+    "\n",
+    "# Read train_log.jsonl\n",
+    "log_rows = []\n",
+    "with open(TRAIN_LOG) as f:\n",
+    "    for line in f:\n",
+    "        if line.strip():\n",
+    "            log_rows.append(json.loads(line))\n",
+    "\n",
+    "step_to_row = {r[\"step\"]: r for r in log_rows}\n",
+    "\n",
+    "# For each candidate checkpoint, compute its avg(last 10) at the step it was saved\n",
+    "def step_for_ckpt(name):\n",
+    "    if name == \"final\":\n",
+    "        return log_rows[-1][\"step\"]\n",
+    "    return int(name.split(\"-\")[1])\n",
+    "\n",
+    "candidate_scores = {}\n",
+    "for ckpt_name in candidate_ckpts:\n",
+    "    ckpt_step = step_for_ckpt(ckpt_name)\n",
+    "    # Window: 10 most recent steps ending at ckpt_step\n",
+    "    window = [r for r in log_rows if r[\"step\"] <= ckpt_step][-10:]\n",
+    "    if not window:\n",
+    "        continue\n",
+    "    mean_r = sum(r[\"mean_reward\"] for r in window) / len(window)\n",
+    "    last_obj = window[-1][\"scheduled_objective\"] if window else \"?\"\n",
+    "    candidate_scores[ckpt_name] = {\n",
+    "        \"step\": ckpt_step,\n",
+    "        \"avg_last_10\": mean_r,\n",
+    "        \"n_steps_in_window\": len(window),\n",
+    "        \"last_objective_at_save\": last_obj,\n",
+    "    }\n",
+    "\n",
+    "# Print all candidates\n",
+    "print(f\"\\n{'CHECKPOINT':<18s} {'STEP':>5s} {'avg(last 10)':>14s}  {'window':>7s}\")\n",
+    "print(\"-\" * 55)\n",
+    "for name, info in candidate_scores.items():\n",
+    "    print(f\"{name:<18s} {info['step']:>5d} {info['avg_last_10']:>14.3f}  {info['n_steps_in_window']:>5d}/10\")\n",
+    "\n",
+    "# Pick winner\n",
+    "best_ckpt = max(candidate_scores.keys(), key=lambda k: candidate_scores[k][\"avg_last_10\"])\n",
+    "br = candidate_scores[best_ckpt]\n",
+    "print(f\"\\n🏆 BEST CHECKPOINT: {best_ckpt}\")\n",
+    "print(f\"   step: {br['step']}\")\n",
+    "print(f\"   avg(last 10): {br['avg_last_10']:.3f}\")\n",
+    "\n",
+    "# Copy best → checkpoints/best/\n",
+    "BEST_DIR = f\"{CHECKPOINTS_DIR}/best\"\n",
+    "if os.path.exists(BEST_DIR):\n",
+    "    shutil.rmtree(BEST_DIR)\n",
+    "shutil.copytree(f\"{CHECKPOINTS_DIR}/{best_ckpt}\", BEST_DIR)\n",
+    "\n",
+    "# Save the selection metadata\n",
+    "with open(f\"{RUN_DIR}/best_checkpoint.json\", \"w\") as f:\n",
+    "    json.dump({\n",
+    "        \"best_checkpoint\": best_ckpt,\n",
+    "        \"selection_method\": \"avg(last 10) from train_log.jsonl\",\n",
+    "        \"all_candidates\": candidate_scores,\n",
+    "    }, f, indent=2)\n",
+    "\n",
+    "# Reload best weights into the live 'default' adapter so subsequent cells\n",
+    "# (plots, HF push) use the winner\n",
+    "weights_path = f\"{BEST_DIR}/adapter_model.safetensors\"\n",
+    "if os.path.isfile(weights_path):\n",
+    "    model.set_adapter(\"default\")\n",
+    "    state_dict = safetensors.torch.load_file(weights_path)\n",
+    "    model.load_state_dict(state_dict, strict=False)\n",
+    "    print(f\"\\n💾 copied {best_ckpt} → {BEST_DIR}\")\n",
+    "    print(f\"💾 wrote {RUN_DIR}/best_checkpoint.json\")\n",
+    "    print(f\"   live 'default' adapter is now using {best_ckpt} weights\")\n",
+    "else:\n",
+    "    print(f\"⚠️  {weights_path} not found — skipping live reload\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c70adc3a",
+   "metadata": {},
+   "source": [
+    "## Cell 19 — Plot: 5-rubric mean curves (the demo highlight)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "50d96bc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "saved → /data/grpo_runs/v3/plots/per_rubric_curves.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "PLOTS_DIR = f\"{RUN_DIR}/plots\"\n",
+    "os.makedirs(PLOTS_DIR, exist_ok=True)\n",
+    "\n",
+    "stats = []\n",
+    "with open(TRAIN_LOG) as f:\n",
+    "    for line in f:\n",
+    "        stats.append(json.loads(line))\n",
+    "steps = [s[\"step\"] for s in stats]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 6))\n",
+    "for rubric in [\"economic\", \"diplomatic\", \"military\", \"stability\", \"hidden\"]:\n",
+    "    series = [s[\"rubric_components_mean\"][rubric] for s in stats]\n",
+    "    ax.plot(steps, series, label=rubric, linewidth=2, alpha=0.85)\n",
+    "ax.set_xlabel(\"training step\")\n",
+    "ax.set_ylabel(\"rubric component (focal mean)\")\n",
+    "ax.set_title(\"v3 — per-rubric learning curves on env v2 (Nexus)\")\n",
+    "ax.legend(loc=\"best\")\n",
+    "ax.grid(alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f\"{PLOTS_DIR}/per_rubric_curves.png\", dpi=120)\n",
+    "plt.show()\n",
+    "print(f\"saved → {PLOTS_DIR}/per_rubric_curves.png\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0cfc449",
+   "metadata": {},
+   "source": [
+    "## Cell 20 — Plot: per-objective reward curves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "46edf221",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "saved → /data/grpo_runs/v3/plots/per_objective_curves.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "\n",
+    "by_obj = defaultdict(list)\n",
+    "for s in stats:\n",
+    "    by_obj[s[\"scheduled_objective\"]].append((s[\"step\"], s[\"mean_reward\"]))\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "for obj in sorted(by_obj.keys()):\n",
+    "    xs = [p[0] for p in by_obj[obj]]\n",
+    "    ys = [p[1] for p in by_obj[obj]]\n",
+    "    ax.plot(xs, ys, \"o-\", label=obj, alpha=0.85)\n",
+    "ax.set_xlabel(\"training step\")\n",
+    "ax.set_ylabel(\"mean trajectory reward\")\n",
+    "ax.set_title(\"v3 — reward grouped by Nexus's hidden objective\")\n",
+    "ax.legend(loc=\"best\", fontsize=8)\n",
+    "ax.grid(alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f\"{PLOTS_DIR}/per_objective_curves.png\", dpi=120)\n",
+    "plt.show()\n",
+    "print(f\"saved → {PLOTS_DIR}/per_objective_curves.png\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29a36c70",
+   "metadata": {},
+   "source": [
+    "## Cell 21 — Plots: reward + KL + diversity + group_std + dense vs final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "da82249a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x800 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "saved → /data/grpo_runs/v3/plots/training_diagnostics.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 8))\n",
+    "\n",
+    "ax1 = axes[0, 0]\n",
+    "ax1.plot(steps, [s[\"mean_reward\"] for s in stats], \"b-\", label=\"mean reward\", linewidth=2)\n",
+    "ax1.set_ylabel(\"mean reward\", color=\"b\")\n",
+    "ax1.set_xlabel(\"step\")\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.plot(steps, [s[\"kl_mean\"] for s in stats], \"r-\", label=\"KL\", linewidth=2, alpha=0.7)\n",
+    "ax2.set_ylabel(\"KL\", color=\"r\")\n",
+    "ax1.set_title(\"reward (blue) + KL (red)\")\n",
+    "ax1.grid(alpha=0.3)\n",
+    "\n",
+    "axes[0, 1].plot(steps, [s[\"distinct_actions\"] for s in stats], \"g-\", linewidth=2)\n",
+    "axes[0, 1].set_xlabel(\"step\"); axes[0, 1].set_ylabel(\"distinct actions\")\n",
+    "axes[0, 1].set_title(\"action diversity (anti-mode-collapse)\")\n",
+    "axes[0, 1].axhline(y=1, color=\"r\", linestyle=\"--\", alpha=0.5, label=\"collapse threshold\")\n",
+    "axes[0, 1].grid(alpha=0.3); axes[0, 1].legend()\n",
+    "\n",
+    "axes[1, 0].plot(steps, [s[\"std_reward\"] for s in stats], \"m-\", linewidth=2)\n",
+    "axes[1, 0].set_xlabel(\"step\"); axes[1, 0].set_ylabel(\"group std (reward)\")\n",
+    "axes[1, 0].set_title(\"group_std (must stay > 0)\")\n",
+    "axes[1, 0].axhline(y=0.02, color=\"r\", linestyle=\"--\", alpha=0.5, label=\"std=0.02 floor\")\n",
+    "axes[1, 0].grid(alpha=0.3); axes[1, 0].legend()\n",
+    "\n",
+    "axes[1, 1].plot(steps, [s[\"dense_mean\"] for s in stats], \"c-\", label=\"dense_mean\", linewidth=2)\n",
+    "axes[1, 1].plot(steps, [s[\"final_mean\"] for s in stats], \"orange\", label=\"final_mean\", linewidth=2)\n",
+    "axes[1, 1].set_xlabel(\"step\"); axes[1, 1].set_ylabel(\"reward\")\n",
+    "axes[1, 1].set_title(\"dense vs final (diverging = potential reward hacking)\")\n",
+    "axes[1, 1].grid(alpha=0.3); axes[1, 1].legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f\"{PLOTS_DIR}/training_diagnostics.png\", dpi=120)\n",
+    "plt.show()\n",
+    "print(f\"saved → {PLOTS_DIR}/training_diagnostics.png\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23187391-c32a-4c08-b1c1-986de4252e66",
+   "metadata": {},
+   "source": [
+    "## Cell 22 — 3-variant baseline comparison eval\n",
+    "\n",
+    "Runs **8 stratified episodes per variant** on the same fixed seeds (one per\n",
+    "hidden objective) to compare:\n",
+    "\n",
+    "| Variant | Focal model | Opponents |\n",
+    "|---|---|---|\n",
+    "| **Base Qwen** | base 7B (`disable_adapter`) | frozen SFT (control) |\n",
+    "| **+ SFT only** | SFT (`frozen_opp` adapter) | frozen SFT |\n",
+    "| **+ SFT + v3 GRPO** | best v3 checkpoint (`default` adapter) | frozen SFT |\n",
+    "\n",
+    "Opponents are held constant at SFT level so the comparison isolates focal-policy\n",
+    "quality. Direct hit on the 20% rubric (\"compare trained vs untrained baseline\").\n",
+    "\n",
+    "Wall clock: ~5 min × 3 variants × 8 episodes = ~25–35 min on L4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "6332f59e-afbc-4add-a689-a104fea9af7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      "=== Eval: Base Qwen 7B (focal_mode=base) ===\n",
+      "============================================================\n",
+      "  KINGMAKER          seed=1000 reward=0.436 final=0.457  hid=0.20  bankrupt=False\n",
+      "  SABOTEUR           seed=1001 reward=0.478 final=0.478  hid=0.27  bankrupt=False\n",
+      "  SURVIVOR           seed=1002 reward=0.758 final=0.825  hid=1.00  bankrupt=False\n",
+      "  TRADE_HEGEMON      seed=1003 reward=0.421 final=0.455  hid=0.00  bankrupt=False\n",
+      "  COALITION_BUILDER  seed=1004 reward=0.590 final=0.695  hid=0.21  bankrupt=False\n",
+      "  LONE_WOLF          seed=1005 reward=0.717 final=0.753  hid=1.00  bankrupt=False\n",
+      "  SPY_MASTER         seed=1006 reward=0.482 final=0.524  hid=0.00  bankrupt=False\n",
+      "  PEACEKEEPER        seed=1007 reward=0.347 final=0.391  hid=0.00  bankrupt=False\n",
+      "  → mean=0.529 ± 0.137  bankrupt=0%\n",
+      "\n",
+      "============================================================\n",
+      "=== Eval: + SFT only (focal_mode=frozen_opp) ===\n",
+      "============================================================\n",
+      "  KINGMAKER          seed=1000 reward=0.360 final=0.394  hid=0.10  bankrupt=False\n",
+      "  SABOTEUR           seed=1001 reward=0.438 final=0.469  hid=0.25  bankrupt=False\n",
+      "  SURVIVOR           seed=1002 reward=0.646 final=0.689  hid=1.00  bankrupt=False\n",
+      "  TRADE_HEGEMON      seed=1003 reward=0.351 final=0.421  hid=0.00  bankrupt=False\n",
+      "  COALITION_BUILDER  seed=1004 reward=0.782 final=0.841  hid=0.96  bankrupt=False\n",
+      "  LONE_WOLF          seed=1005 reward=0.436 final=0.381  hid=0.58  bankrupt=False\n",
+      "  SPY_MASTER         seed=1006 reward=0.644 final=0.723  hid=0.88  bankrupt=False\n",
+      "  PEACEKEEPER        seed=1007 reward=0.349 final=0.405  hid=0.00  bankrupt=False\n",
+      "  → mean=0.501 ± 0.156  bankrupt=0%\n",
+      "\n",
+      "============================================================\n",
+      "=== Eval: + SFT + v3 GRPO (focal_mode=default) ===\n",
+      "============================================================\n",
+      "  KINGMAKER          seed=1000 reward=0.620 final=0.622  hid=0.42  bankrupt=False\n",
+      "  SABOTEUR           seed=1001 reward=0.422 final=0.439  hid=0.33  bankrupt=False\n",
+      "  SURVIVOR           seed=1002 reward=0.722 final=0.790  hid=1.00  bankrupt=False\n",
+      "  TRADE_HEGEMON      seed=1003 reward=0.371 final=0.435  hid=0.00  bankrupt=False\n",
+      "  COALITION_BUILDER  seed=1004 reward=0.825 final=0.884  hid=0.96  bankrupt=False\n",
+      "  LONE_WOLF          seed=1005 reward=0.473 final=0.506  hid=0.08  bankrupt=False\n",
+      "  SPY_MASTER         seed=1006 reward=0.244 final=0.000  hid=0.88  bankrupt=True\n",
+      "  PEACEKEEPER        seed=1007 reward=0.540 final=0.598  hid=0.00  bankrupt=False\n",
+      "  → mean=0.527 ± 0.178  bankrupt=12%\n",
+      "\n",
+      "==============================================================================\n",
+      "VARIANT                       MEAN     STD   BANKR   Δ vs Base  COMPONENTS\n",
+      "------------------------------------------------------------------------------\n",
+      "Base Qwen 7B                 0.529   0.137      0%  +    +0.000  eco=0.68  dip=0.32  mil=0.46  sta=0.77  hid=0.33\n",
+      "+ SFT only                   0.501   0.156      0%      -0.028  eco=0.56  dip=0.34  mil=0.31  sta=0.63  hid=0.47\n",
+      "+ SFT + v3 GRPO              0.527   0.178     12%      -0.001  eco=0.59  dip=0.56  mil=0.38  sta=0.65  hid=0.46\n",
+      "==============================================================================\n",
+      "\n",
+      "💾 saved eval results → /data/grpo_runs/v3/eval_3variants.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "# === Eval rollout helper that supports any focal adapter mode ===\n",
+    "def generate_with_mode(prompts, mode, max_new_tokens=128, temperature=0.7):\n",
+    "    \"\"\"mode: 'base' (no adapter), 'frozen_opp' (SFT), 'default' (trained).\"\"\"\n",
+    "    if isinstance(prompts, str):\n",
+    "        prompts = [prompts]\n",
+    "    if not prompts:\n",
+    "        return []\n",
+    "    inputs = tokenizer(prompts, return_tensors=\"pt\", padding=True, truncation=True,\n",
+    "                       max_length=MAX_SEQ_LENGTH).to(\"cuda\")\n",
+    "    if mode == \"base\":\n",
+    "        with model.disable_adapter(), torch.no_grad():\n",
+    "            outputs = model.generate(**inputs, max_new_tokens=max_new_tokens,\n",
+    "                                     do_sample=True, temperature=temperature,\n",
+    "                                     pad_token_id=tokenizer.eos_token_id)\n",
+    "    else:\n",
+    "        model.set_adapter(mode)\n",
+    "        with torch.no_grad():\n",
+    "            outputs = model.generate(**inputs, max_new_tokens=max_new_tokens,\n",
+    "                                     do_sample=True, temperature=temperature,\n",
+    "                                     pad_token_id=tokenizer.eos_token_id)\n",
+    "    plen = inputs.input_ids.shape[1]\n",
+    "    return [tokenizer.decode(o[plen:], skip_special_tokens=True) for o in outputs]\n",
+    "\n",
+    "\n",
+    "def play_eval_rollout(env, focal_mode, country_id=FOCAL_COUNTRY, temperature=0.7):\n",
+    "    \"\"\"Like play_one_rollout_v3 but with configurable focal-mode.\n",
+    "\n",
+    "    Opponents always use 'frozen_opp' (SFT) so the comparison isolates focal quality.\n",
+    "    \"\"\"\n",
+    "    history = []\n",
+    "    while not env.state.done:\n",
+    "        obs_dict = {cid: env.get_observation(cid).model_dump() for cid in env.active_country_ids}\n",
+    "        prompts = {cid: _build_chat(obs_dict[cid], cid) for cid in env.active_country_ids}\n",
+    "\n",
+    "        # Focal uses focal_mode\n",
+    "        action_dicts = {}\n",
+    "        out = generate_with_mode([prompts[country_id]], mode=focal_mode, temperature=temperature)\n",
+    "        action_dicts[country_id] = parse_action(out[0], country_id)\n",
+    "        # Opponents = frozen SFT (control)\n",
+    "        others = [cid for cid in env.active_country_ids if cid != country_id]\n",
+    "        if others:\n",
+    "            out = generate_with_mode([prompts[cid] for cid in others],\n",
+    "                                     mode=\"frozen_opp\", temperature=temperature)\n",
+    "            for cid, c in zip(others, out):\n",
+    "                action_dicts[cid] = parse_action(c, cid)\n",
+    "\n",
+    "        # Build GeoActions and step\n",
+    "        actions = {}\n",
+    "        for cid in env.active_country_ids:\n",
+    "            ad = action_dicts.get(cid, {\"action_type\": \"WAIT\"}) or {\"action_type\": \"WAIT\"}\n",
+    "            ad[\"source_country\"] = cid\n",
+    "            try:\n",
+    "                actions[cid] = GeoAction(**ad)\n",
+    "            except Exception:\n",
+    "                actions[cid] = GeoAction(action_type=\"WAIT\", source_country=cid)\n",
+    "        results = env.step_all(actions)\n",
+    "        first = env.active_country_ids[0]\n",
+    "        history.append({\n",
+    "            \"turn\": env.state.current_turn,\n",
+    "            \"rewards\": {cid: results[cid].reward for cid in env.active_country_ids},\n",
+    "            \"actions\": {cid: actions[cid].model_dump() for cid in env.active_country_ids},\n",
+    "            \"components\": {cid: results[cid].metadata.get(\"reward_components\", {}) for cid in env.active_country_ids},\n",
+    "        })\n",
+    "\n",
+    "    # Trajectory reward (same formula as training)\n",
+    "    turn_rewards = [t[\"rewards\"][country_id] for t in history if country_id in t[\"rewards\"]]\n",
+    "    dense = sum(turn_rewards) / max(len(turn_rewards), 1)\n",
+    "    final = env.grade_country(country_id)\n",
+    "    traj = DENSE_WEIGHT * dense + FINAL_WEIGHT * final\n",
+    "    rubric_names = [\"economic\", \"diplomatic\", \"military\", \"stability\", \"hidden\"]\n",
+    "    components_traj = {n: [t[\"components\"][country_id].get(n, 0.0) for t in history] for n in rubric_names}\n",
+    "    return history, traj, dense, final, components_traj\n",
+    "\n",
+    "\n",
+    "# === Eval config ===\n",
+    "EVAL_SEEDS_BASELINE = [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007]\n",
+    "EVAL_OBJECTIVES_BASELINE = list(OBJECTIVE_POOL)  # 8 objectives in fixed order\n",
+    "assert len(EVAL_SEEDS_BASELINE) == len(EVAL_OBJECTIVES_BASELINE) == 8\n",
+    "\n",
+    "VARIANTS = [\n",
+    "    (\"base\",     \"Base Qwen 7B\",         \"base\"),\n",
+    "    (\"sft_only\", \"+ SFT only\",           \"frozen_opp\"),\n",
+    "    (\"sft_grpo\", \"+ SFT + v3 GRPO\",      \"default\"),\n",
+    "]\n",
+    "\n",
+    "FastLanguageModel.for_inference(model)\n",
+    "baseline_results = {}\n",
+    "\n",
+    "for v_key, v_label, focal_mode in VARIANTS:\n",
+    "    print(f\"\\n{'='*60}\")\n",
+    "    print(f\"=== Eval: {v_label} (focal_mode={focal_mode}) ===\")\n",
+    "    print('='*60)\n",
+    "    rollouts_for_variant = []\n",
+    "    for seed, obj in zip(EVAL_SEEDS_BASELINE, EVAL_OBJECTIVES_BASELINE):\n",
+    "        env.reset(task_id=\"task3\", seed=seed)\n",
+    "        env.countries[FOCAL_COUNTRY].hidden_objective = obj\n",
+    "        h, traj_r, dense, final_g, comps = play_eval_rollout(\n",
+    "            env, focal_mode=focal_mode, country_id=FOCAL_COUNTRY, temperature=0.7\n",
+    "        )\n",
+    "        rollouts_for_variant.append({\n",
+    "            \"objective\": obj, \"seed\": seed,\n",
+    "            \"reward\": traj_r, \"final_grade\": final_g, \"dense_reward\": dense,\n",
+    "            \"components\": {k: float(np.mean(v)) if v else 0.0 for k, v in comps.items()},\n",
+    "            \"bankrupt\": env.countries[FOCAL_COUNTRY].is_bankrupt,\n",
+    "            \"n_turns\": len(h),\n",
+    "        })\n",
+    "        print(f\"  {obj:18s} seed={seed} reward={traj_r:.3f} final={final_g:.3f}  \"\n",
+    "              f\"hid={rollouts_for_variant[-1]['components']['hidden']:.2f}  \"\n",
+    "              f\"bankrupt={rollouts_for_variant[-1]['bankrupt']}\")\n",
+    "\n",
+    "    rewards = [r[\"reward\"] for r in rollouts_for_variant]\n",
+    "    baseline_results[v_key] = {\n",
+    "        \"label\": v_label, \"focal_mode\": focal_mode,\n",
+    "        \"mean_reward\": float(np.mean(rewards)),\n",
+    "        \"std_reward\":  float(np.std(rewards)),\n",
+    "        \"bankruptcy_rate\": sum(r[\"bankrupt\"] for r in rollouts_for_variant) / len(rollouts_for_variant),\n",
+    "        \"rubric_means\": {\n",
+    "            k: float(np.mean([r[\"components\"][k] for r in rollouts_for_variant]))\n",
+    "            for k in [\"economic\", \"diplomatic\", \"military\", \"stability\", \"hidden\"]\n",
+    "        },\n",
+    "        \"per_objective\": {r[\"objective\"]: r[\"reward\"] for r in rollouts_for_variant},\n",
+    "        \"per_objective_hidden\": {r[\"objective\"]: r[\"components\"][\"hidden\"] for r in rollouts_for_variant},\n",
+    "        \"rollouts\": rollouts_for_variant,\n",
+    "    }\n",
+    "    print(f\"  → mean={baseline_results[v_key]['mean_reward']:.3f} ± \"\n",
+    "          f\"{baseline_results[v_key]['std_reward']:.3f}  \"\n",
+    "          f\"bankrupt={baseline_results[v_key]['bankruptcy_rate']*100:.0f}%\")\n",
+    "\n",
+    "# Reset to default adapter for any subsequent cells\n",
+    "model.set_adapter(\"default\")\n",
+    "\n",
+    "# === Print summary table ===\n",
+    "base_r = baseline_results[\"base\"][\"mean_reward\"]\n",
+    "print(f\"\\n{'='*78}\")\n",
+    "print(f\"{'VARIANT':<25s} {'MEAN':>8s} {'STD':>7s} {'BANKR':>7s}  {'Δ vs Base':>10s}  {'COMPONENTS'}\")\n",
+    "print('-' * 78)\n",
+    "for v_key, _, _ in VARIANTS:\n",
+    "    r = baseline_results[v_key]\n",
+    "    delta = r[\"mean_reward\"] - base_r\n",
+    "    sign = \"+\" if delta >= 0 else \"\"\n",
+    "    comps = \"  \".join(f\"{k[:3]}={v:.2f}\" for k, v in r[\"rubric_means\"].items())\n",
+    "    print(f\"{r['label']:<25s} {r['mean_reward']:>8.3f} {r['std_reward']:>7.3f} \"\n",
+    "          f\"{r['bankruptcy_rate']*100:>6.0f}%  {sign}{delta:>+10.3f}  {comps}\")\n",
+    "print('=' * 78)\n",
+    "\n",
+    "# === Save ===\n",
+    "with open(f\"{RUN_DIR}/eval_3variants.json\", \"w\") as f:\n",
+    "    json.dump(baseline_results, f, indent=2, default=str)\n",
+    "print(f\"\\n💾 saved eval results → {RUN_DIR}/eval_3variants.json\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "4c105f90-17a6-43f5-9afd-2ebeee6613ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid character '§' (U+00A7) (1824786823.py, line 4)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[31], line 4\u001b[0;36m\u001b[0m\n\u001b[0;31m    1. Aggregate 3-bar chart (Plot 4 in BLOG_STRATEGY.md §3.5a)\u001b[0m\n\u001b[0m                                                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid character '§' (U+00A7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## Cell 23 — Plots: 3-variant baseline comparison\n",
+    "\n",
+    "Three figures:\n",
+    "1. Aggregate 3-bar chart (Plot 4 in BLOG_STRATEGY.md §3.5a)\n",
+    "2. Per-objective grouped bars (Plot 5)\n",
+    "3. Per-rubric breakdown bars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "67d59edd-fedf-442e-b8aa-c6978c26ce38",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 900x550 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "saved → /data/grpo_runs/v3/plots/eval_aggregate_bars.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "# === Plot 4: aggregate 3-bar chart ===\n",
+    "labels = [baseline_results[k][\"label\"] for k, _, _ in VARIANTS]\n",
+    "means  = [baseline_results[k][\"mean_reward\"] for k, _, _ in VARIANTS]\n",
+    "stds   = [baseline_results[k][\"std_reward\"] for k, _, _ in VARIANTS]\n",
+    "colors = [\"#d62728\", \"#ff7f0e\", \"#2ca02c\"]  # red, orange, green\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(9, 5.5))\n",
+    "bars = ax.bar(labels, means, yerr=stds, capsize=8, color=colors, alpha=0.88,\n",
+    "              edgecolor=\"black\", linewidth=1.2)\n",
+    "for bar, m, s in zip(bars, means, stds):\n",
+    "    ax.text(bar.get_x() + bar.get_width()/2, m + s + 0.02,\n",
+    "            f\"{m:.3f}\\n±{s:.3f}\", ha=\"center\", fontsize=10, fontweight=\"bold\")\n",
+    "ax.set_ylabel(\"Mean trajectory reward (8 stratified episodes)\", fontsize=11)\n",
+    "ax.set_title(\"3-variant comparison on env v2 task3 (Nexus, hidden objectives stratified)\",\n",
+    "             fontsize=12)\n",
+    "ax.set_ylim(0, max(means) + max(stds) + 0.18)\n",
+    "ax.grid(axis=\"y\", alpha=0.3)\n",
+    "\n",
+    "# Annotate deltas vs base\n",
+    "base_r = means[0]\n",
+    "for i, m in enumerate(means[1:], start=1):\n",
+    "    delta = m - base_r\n",
+    "    sign = \"+\" if delta >= 0 else \"\"\n",
+    "    ax.annotate(f\"{sign}{delta:.3f} vs base\", xy=(i, m), xytext=(i, m - 0.06),\n",
+    "                ha=\"center\", fontsize=9, color=\"darkblue\", fontweight=\"bold\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f\"{PLOTS_DIR}/eval_aggregate_bars.png\", dpi=120, bbox_inches=\"tight\")\n",
+    "plt.show()\n",
+    "print(f\"saved → {PLOTS_DIR}/eval_aggregate_bars.png\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74a5ddbe",
+   "metadata": {},
+   "source": [
+    "## Cell 24 — Push the entire run dir (checkpoints + logs + plots + rollouts) to HuggingFace\n",
+    "\n",
+    "Uploads the full `RUN_DIR` tree to `adityadas14/nexus-grpo-v3` (or override via\n",
+    "`HF_REPO_V3` env var). Includes:\n",
+    "- All checkpoints: `checkpoints/checkpoint-{10,20,30,40,50,final,best}/`\n",
+    "- Training artifacts: `train_log.jsonl`, `run_config.json`, `raw_log.txt`,\n",
+    "  `sample_completions.jsonl`, `per_country.jsonl`\n",
+    "- Eval results: `best_checkpoint.json`, `eval_3variants.json`\n",
+    "- Per-step rollouts: `rollouts/step_*.jsonl`\n",
+    "- All plots: `plots/*.png`\n",
+    "\n",
+    "Estimated upload: ~700–800 MB total (~50 MB per LoRA × 7 checkpoints + small files).\n",
+    "Wall clock: ~5–10 min on HF Hub.\n",
+    "\n",
+    "Set `HF_TOKEN` before running."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "5c0ee27f-b37d-492c-a0eb-972454965f9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"HF_TOKEN\"] = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6c6fb52a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "writing model card → /data/grpo_runs/v3/README.md\n",
+      "\n",
+      "uploading /data/grpo_runs/v3 → https://huggingface.co/adityadas14/nexus-grpo-v3\n",
+      "(this can take 5–10 min; ~700 MB across 7 checkpoints + logs + plots)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97a6a961eb0d43ba8b36933ed6174b0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing Files (0 / 0): |          |  0.00B /  0.00B            "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "766ae6ad0d5041b893b7eda5f31e30a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "New Data Upload: |          |  0.00B /  0.00B            "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✅ pushed to https://huggingface.co/adityadas14/nexus-grpo-v3\n",
+      "   files at: https://huggingface.co/adityadas14/nexus-grpo-v3/tree/main\n",
+      "\n",
+      "Uploaded contents:\n",
+      "  README.md                                      1.9 KB\n",
+      "  best_checkpoint.json                           1.1 KB\n",
+      "  eval_3variants.json                           13.5 KB\n",
+      "  per_country.jsonl                             60.4 KB\n",
+      "  raw_log.txt                                    6.0 KB\n",
+      "  run_config.json                                1.4 KB\n",
+      "  sample_completions.jsonl                      32.1 KB\n",
+      "  train_log.jsonl                               57.8 KB\n",
+      "  checkpoints/  (0 files,   0.0 MB)\n",
+      "  checkpoints/checkpoint-10/  (7 files, 173.0 MB)\n",
+      "  checkpoints/checkpoint-10/frozen_opp/  (2 files, 161.5 MB)\n",
+      "  checkpoints/checkpoint-20/  (7 files, 173.0 MB)\n",
+      "  checkpoints/checkpoint-20/frozen_opp/  (2 files, 161.5 MB)\n",
+      "  checkpoints/checkpoint-30/  (7 files, 173.0 MB)\n",
+      "  checkpoints/checkpoint-30/frozen_opp/  (2 files, 161.5 MB)\n",
+      "  checkpoints/checkpoint-40/  (7 files, 173.0 MB)\n",
+      "  checkpoints/checkpoint-40/frozen_opp/  (2 files, 161.5 MB)\n",
+      "  checkpoints/checkpoint-50/  (7 files, 173.0 MB)\n",
+      "  checkpoints/checkpoint-50/frozen_opp/  (2 files, 161.5 MB)\n",
+      "  checkpoints/final/  (6 files, 173.0 MB)\n",
+      "  checkpoints/final/frozen_opp/  (2 files, 161.5 MB)\n",
+      "  checkpoints/best/  (7 files, 173.0 MB)\n",
+      "  checkpoints/best/frozen_opp/  (2 files, 161.5 MB)\n",
+      "  rollouts/  (10 files,   7.9 MB)\n",
+      "  .ipynb_checkpoints/  (2 files,   0.0 MB)\n",
+      "  plots/  (11 files,   1.9 MB)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from huggingface_hub import HfApi, create_repo\n",
+    "import os\n",
+    "\n",
+    "HF_REPO_OUT = os.environ.get(\"HF_REPO_V3\", \"adityadas14/nexus-grpo-v3\")\n",
+    "\n",
+    "# Auto-generate a README.md inside RUN_DIR before upload (model card)\n",
+    "readme_md = f'''---\n",
+    "license: apache-2.0\n",
+    "tags:\n",
+    "  - grpo\n",
+    "  - openenv\n",
+    "  - geopolicy\n",
+    "  - multi-agent\n",
+    "  - hidden-objectives\n",
+    "base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit\n",
+    "---\n",
+    "\n",
+    "# Nexus GRPO v3 — env v2 (hidden objectives + composable rubrics)\n",
+    "\n",
+    "Qwen 2.5 7B (LoRA) trained on the [GeoPolicy env v2](https://huggingface.co/spaces/adityadas14/geopolicy)\n",
+    "using GRPO with stratified hidden objectives and frozen-LoRA opponents.\n",
+    "\n",
+    "## Files\n",
+    "\n",
+    "| Path | Contents |\n",
+    "|---|---|\n",
+    "| `checkpoints/best/` | Highest-`avg(last 10)` checkpoint — recommended adapter |\n",
+    "| `checkpoints/checkpoint-{{10,20,30,40,50}}/` | Intermediate checkpoints (every 10 steps) |\n",
+    "| `checkpoints/final/` | Final-step adapter |\n",
+    "| `train_log.jsonl` | Per-step metrics: reward, KL, rubric components, action mix |\n",
+    "| `eval_3variants.json` | 3-variant baseline comparison (Base / +SFT / +SFT+GRPO) |\n",
+    "| `best_checkpoint.json` | Auto-selected best checkpoint metadata |\n",
+    "| `rollouts/step_*.jsonl` | Full per-rollout transcripts (every 5 steps) |\n",
+    "| `plots/` | Training + eval figures (PNG) |\n",
+    "| `run_config.json` | Hyperparameters and run config |\n",
+    "\n",
+    "## Training summary\n",
+    "\n",
+    "- **Algorithm:** GRPO, group_size=4, 50 steps\n",
+    "- **Hyperparams:** LR=1e-5, β=0.01, grad_clip=10, temp=1.0\n",
+    "- **Reward:** `0.5×mean(per_turn) + 0.5×final_grade`, both via `TaskRubric` blend\n",
+    "- **SFT init:** local SFT on 200 balanced demonstrations (3 epochs)\n",
+    "- **Opponents:** frozen-LoRA (Option B) — other 4 countries play with frozen SFT\n",
+    "- **Stratification:** Nexus rotated through all 8 hidden objectives (~6 each)\n",
+    "\n",
+    "See `run_config.json` for exact values.\n",
+    "\n",
+    "## Use the best checkpoint\n",
+    "\n",
+    "```python\n",
+    "from peft import PeftModel\n",
+    "from unsloth import FastLanguageModel\n",
+    "model, tokenizer = FastLanguageModel.from_pretrained(\"unsloth/Qwen2.5-7B-Instruct-bnb-4bit\", ...)\n",
+    "model.load_adapter(\"{HF_REPO_OUT}\", subfolder=\"checkpoints/best\", adapter_name=\"default\")\n",
+    "model.set_adapter(\"default\")\n",
+    "```\n",
+    "'''\n",
+    "\n",
+    "with open(f\"{RUN_DIR}/README.md\", \"w\") as f:\n",
+    "    f.write(readme_md)\n",
+    "\n",
+    "print(f\"writing model card → {RUN_DIR}/README.md\")\n",
+    "\n",
+    "try:\n",
+    "    create_repo(HF_REPO_OUT, exist_ok=True, repo_type=\"model\")\n",
+    "    api = HfApi()\n",
+    "\n",
+    "    # Upload the entire RUN_DIR in one shot (recursive).\n",
+    "    # Excludes optimizer states and OS noise to keep the repo lean.\n",
+    "    print(f\"\\nuploading {RUN_DIR} → https://huggingface.co/{HF_REPO_OUT}\")\n",
+    "    print(\"(this can take 5–10 min; ~700 MB across 7 checkpoints + logs + plots)\")\n",
+    "\n",
+    "    api.upload_folder(\n",
+    "        folder_path=RUN_DIR,\n",
+    "        repo_id=HF_REPO_OUT,\n",
+    "        repo_type=\"model\",\n",
+    "        commit_message=f\"v3 GRPO run — Nexus on env v2 ({datetime.datetime.now().isoformat(timespec='seconds')})\",\n",
+    "        ignore_patterns=[\n",
+    "            \"*.pt\",                  # PyTorch optimizer states (large + reloadable)\n",
+    "            \"optimizer*\",\n",
+    "            \"scheduler*\",\n",
+    "            \"rng_state*\",\n",
+    "            \"*.tmp\",\n",
+    "            \".DS_Store\",\n",
+    "            \"__pycache__/*\",\n",
+    "            \"*.pyc\",\n",
+    "        ],\n",
+    "    )\n",
+    "\n",
+    "    print(f\"\\n✅ pushed to https://huggingface.co/{HF_REPO_OUT}\")\n",
+    "    print(f\"   files at: https://huggingface.co/{HF_REPO_OUT}/tree/main\")\n",
+    "\n",
+    "    # Print final summary of what was uploaded\n",
+    "    print(\"\\nUploaded contents:\")\n",
+    "    for root, dirs, files in os.walk(RUN_DIR):\n",
+    "        rel = os.path.relpath(root, RUN_DIR)\n",
+    "        if rel == \".\":\n",
+    "            for f in sorted(files):\n",
+    "                size_kb = os.path.getsize(os.path.join(root, f)) / 1024\n",
+    "                print(f\"  {f:40s}  {size_kb:>8.1f} KB\")\n",
+    "        else:\n",
+    "            n_files = len(files)\n",
+    "            total_mb = sum(os.path.getsize(os.path.join(root, f)) for f in files) / 1e6\n",
+    "            print(f\"  {rel}/  ({n_files} files, {total_mb:>5.1f} MB)\")\n",
+    "\n",
+    "except Exception as e:\n",
+    "    print(f\"❌ upload failed: {e}\")\n",
+    "    print(\"\\ntroubleshooting:\")\n",
+    "    print(\"  1. Set HF_TOKEN env var: export HF_TOKEN=hf_...\")\n",
+    "    print(\"  2. Check write permissions on the repo\")\n",
+    "    print(\"  3. Verify the repo name is valid\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e75a2ab2-9eef-4edb-a2b2-2351f5334b03",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}