| """Append Phase 4 Level 2 training cells to training/sanity_run.ipynb.""" |
| import json |
| import pathlib |
|
|
| NB_PATH = pathlib.Path("training/sanity_run.ipynb") |
|
|
| with open(NB_PATH, encoding="utf-8") as f: |
| nb = json.load(f) |
|
|
| new_cells = [ |
| { |
| "cell_type": "markdown", |
| "id": "phase4-header", |
| "metadata": {}, |
| "source": "## Phase 4 — Level 2 Training (run after Level 1 sanity confirmed)\n\nLevel 2 introduces distractor context: each observation contains 2 plausible-but-false statements the model must resist. The reward structure is identical to Level 1." |
| }, |
| { |
| "cell_type": "code", |
| "id": "phase4-config", |
| "metadata": {}, |
| "outputs": [], |
| "execution_count": None, |
| "source": "# ============================================================\n# PHASE 4 CONFIG — Level 2 Training\n# ============================================================\nLEVEL2_STEPS = 80\nLEVEL2_ROLLOUTS_PER_PROMPT = 4\nLEVEL2_BATCH_SIZE = 2\nLEVEL2_LEARNING_RATE = 5e-6\n\n# Same base URL as sanity run — just passing level=2 in reset calls\nENV_BASE_URL_L2 = ENV_BASE_URL # defined in cell-2 above\n\nprint(f'Phase 4 config loaded. Level2 Steps={LEVEL2_STEPS}, ENV={ENV_BASE_URL_L2}')" |
| }, |
| { |
| "cell_type": "code", |
| "id": "phase4-dataset", |
| "metadata": {}, |
| "outputs": [], |
| "execution_count": None, |
| "source": "import json as _json2\nimport pathlib as _pathlib2\n\n# Load level2 questions (must have run generate_distractors.py first)\ntry:\n import deceit_env as _de\n _l2_path = _pathlib2.Path(_de.__file__).parent / 'data' / 'level2.jsonl'\n l2_questions = []\n with open(_l2_path) as _f:\n for _line in _f:\n _line = _line.strip()\n if _line:\n l2_questions.append(_json2.loads(_line))\nexcept Exception as _e:\n print(f'Could not load level2 from package: {_e}')\n import urllib.request as _ur\n _url = 'https://raw.githubusercontent.com/Jayant-kernel/DECEIT-the-ai-truth-environment-/main/src/deceit_env/data/level2.jsonl'\n l2_questions = []\n with _ur.urlopen(_url) as _resp:\n for _line in _resp.read().decode().splitlines():\n if _line.strip():\n l2_questions.append(_json2.loads(_line))\n\nprint(f'Loaded {len(l2_questions)} Level 2 questions')\n\n\ndef make_l2_prompt(q: str, context: list[str]) -> str:\n context_block = '\\n'.join(context)\n user_content = f'Question: {q}\\n\\nContext:\\n{context_block}\\n\\nTurn 1 of 3. Respond in JSON.'\n messages = [\n {'role': 'system', 'content': SYSTEM_PROMPT},\n {'role': 'user', 'content': user_content},\n ]\n return tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n\n\nl2_dataset_rows = [\n {'prompt': make_l2_prompt(q['question'], q['distractors']), 'question': q['question']}\n for q in l2_questions\n]\nl2_train_dataset = Dataset.from_list(l2_dataset_rows)\nprint(f'Level 2 dataset ready: {len(l2_train_dataset)} prompts')" |
| }, |
| { |
| "cell_type": "code", |
| "id": "phase4-reward-fn", |
| "metadata": {}, |
| "outputs": [], |
| "execution_count": None, |
| "source": "def grpo_reward_fn_l2(completions, prompts=None, **kwargs):\n \"\"\"GRPO reward function for Level 2: resets env with level=2.\"\"\"\n rewards = []\n parse_fail_count = 0\n\n for completion_text in completions:\n try:\n action = parse_action(completion_text)\n except Exception:\n action = PARSE_FAIL_ACTION.copy()\n parse_fail_count += 1\n\n try:\n with _env_lock:\n # Level 2 reset\n reset_resp = requests.post(\n f'{ENV_BASE_URL_L2}/reset',\n json={'level': 2},\n timeout=30,\n )\n reset_resp.raise_for_status()\n obs = reset_resp.json()\n obs_data = obs.get('observation', obs)\n max_turns = obs_data.get('max_turns', 3)\n question = obs_data.get('question', '')\n context = obs_data.get('context', [])\n\n total_reward = 0.0\n current_action = action\n\n for turn in range(max_turns):\n if turn == max_turns - 1:\n current_action['is_final'] = True\n\n step_resp = requests.post(\n f'{ENV_BASE_URL_L2}/step',\n json={'action': current_action},\n timeout=30,\n )\n step_resp.raise_for_status()\n step_obs = step_resp.json()\n step_obs_data = step_obs.get('observation', step_obs)\n\n reward = step_obs.get('reward', 0.0) or 0.0\n done = step_obs.get('done', False)\n context = step_obs_data.get('context', [])\n total_reward += reward\n\n if done:\n break\n\n context_str = '\\n'.join(context)\n user_content = f'Question: {question}\\n\\n{context_str}\\n\\nTurn {turn+2} of {max_turns}. Respond in JSON.'\n messages = [\n {'role': 'system', 'content': SYSTEM_PROMPT},\n {'role': 'user', 'content': user_content},\n ]\n next_prompt = tokenizer.apply_chat_template(\n messages, tokenize=False, add_generation_prompt=True\n )\n inputs = tokenizer(next_prompt, return_tensors='pt').to(model.device)\n with torch.no_grad():\n out_ids = model.generate(\n **inputs, max_new_tokens=256,\n do_sample=False,\n pad_token_id=tokenizer.eos_token_id,\n )\n next_text = tokenizer.decode(\n out_ids[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True\n )\n try:\n current_action = parse_action(next_text)\n except Exception:\n current_action = PARSE_FAIL_ACTION.copy()\n\n except Exception as e:\n print(f' [l2_reward_fn] Episode error: {e}')\n total_reward = -1.3\n\n rewards.append(total_reward)\n\n if parse_fail_count > 0:\n print(f' [l2_reward_fn] Parse failures: {parse_fail_count}/{len(completions)}')\n\n return rewards\n\n\nprint('Level 2 GRPO reward function ready.')" |
| }, |
| { |
| "cell_type": "code", |
| "id": "phase4-train", |
| "metadata": {}, |
| "outputs": [], |
| "execution_count": None, |
| "source": "FastLanguageModel.for_training(model)\n\nl2_run = wandb.init(\n project=WANDB_PROJECT,\n name=f'level2-qwen0.5b',\n config={\n 'model': MODEL_NAME,\n 'level': 2,\n 'training_steps': LEVEL2_STEPS,\n 'rollouts_per_prompt': LEVEL2_ROLLOUTS_PER_PROMPT,\n 'batch_size': LEVEL2_BATCH_SIZE,\n 'learning_rate': LEVEL2_LEARNING_RATE,\n 'env': ENV_BASE_URL_L2,\n },\n)\n\nl2_grpo_config = GRPOConfig(\n output_dir='./deceit-grpo-level2',\n num_train_epochs=1,\n max_steps=LEVEL2_STEPS,\n per_device_train_batch_size=LEVEL2_BATCH_SIZE,\n num_generations=LEVEL2_ROLLOUTS_PER_PROMPT,\n learning_rate=LEVEL2_LEARNING_RATE,\n warmup_steps=5,\n logging_steps=1,\n save_steps=40,\n report_to='wandb',\n max_completion_length=256,\n remove_unused_columns=False,\n)\n\nl2_trainer = GRPOTrainer(\n model=model,\n processing_class=tokenizer,\n reward_funcs=[grpo_reward_fn_l2],\n args=l2_grpo_config,\n train_dataset=l2_train_dataset,\n)\n\nprint(f'Starting Level 2 GRPO training: {LEVEL2_STEPS} steps')\nl2_trainer.train()\nprint('Level 2 training complete.')\nwandb.finish()" |
| }, |
| ] |
|
|
| nb["cells"].extend(new_cells) |
|
|
| with open(NB_PATH, "w", encoding="utf-8") as f: |
| json.dump(nb, f, indent=1, ensure_ascii=False) |
|
|
| print(f"Appended {len(new_cells)} cells to {NB_PATH}") |
| print(f"Total cells now: {len(nb['cells'])}") |
|
|
