{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[],"dockerImageVersionId":31329,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install -q --upgrade \"torch>=2.8.0\" \"triton>=3.4.0\" bitsandbytes \"transformers==4.56.2\" trackio\n!pip install -q \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\"\n!pip install -q \"unsloth[base] @ git+https://github.com/unslothai/unsloth\"\n!pip install -q openenv-core trl httpx nest_asyncio datasets huggingface_hub\nprint('✓ Dependencies installed')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-04-30T18:33:29.198993Z","iopub.execute_input":"2026-04-30T18:33:29.199328Z","iopub.status.idle":"2026-04-30T18:38:07.537017Z","shell.execute_reply.started":"2026-04-30T18:33:29.199296Z","shell.execute_reply":"2026-04-30T18:38:07.535845Z"}},"outputs":[{"name":"stdout","text":"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.1/40.1 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.6/11.6 MB\u001b[0m \u001b[31m93.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m0:01\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m530.7/530.7 MB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta 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This behaviour is the source of the following dependency conflicts.\nlibcuml-cu12 26.2.0 requires cuda-toolkit[cublas,cufft,curand,cusolver,cusparse]==12.*, but you have cuda-toolkit 13.0.2 which is incompatible.\ncuml-cu12 26.2.0 requires cuda-toolkit[cublas,cufft,curand,cusolver,cusparse]==12.*, but you have cuda-toolkit 13.0.2 which is incompatible.\ncuda-python 12.9.4 requires cuda-bindings~=12.9.4, but you have cuda-bindings 13.2.0 which is incompatible.\ntorchaudio 2.10.0+cu128 requires torch==2.10.0, but you have torch 2.11.0 which is incompatible.\ntorchvision 0.25.0+cu128 requires torch==2.10.0, but you have torch 2.11.0 which is incompatible.\ncudf-cu12 26.2.1 requires cuda-toolkit[nvcc,nvrtc]==12.*, but you have cuda-toolkit 13.0.2 which is incompatible.\nlibcuvs-cu12 26.2.0 requires cuda-toolkit[cublas,curand,cusolver,cusparse]==12.*, but you have cuda-toolkit 13.0.2 which is incompatible.\nlibraft-cu12 26.2.0 requires cuda-toolkit[cublas,curand,cusolver,cusparse]==12.*, but you have cuda-toolkit 13.0.2 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0m Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m506.8/506.8 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m915.6/915.6 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.2/12.2 MB\u001b[0m \u001b[31m52.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m52.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m423.1/423.1 kB\u001b[0m \u001b[31m30.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m54.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.0/225.0 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m185.2/185.2 kB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.7/119.7 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.3/199.3 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25h Building wheel for unsloth_zoo (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\nbigframes 2.35.0 requires google-cloud-bigquery-storage<3.0.0,>=2.30.0, which is not installed.\ns3fs 2026.2.0 requires fsspec==2026.2.0, but you have fsspec 2025.9.0 which is incompatible.\ncuml-cu12 26.2.0 requires cuda-toolkit[cublas,cufft,curand,cusolver,cusparse]==12.*, but you have cuda-toolkit 13.0.2 which is incompatible.\ncudf-cu12 26.2.1 requires cuda-toolkit[nvcc,nvrtc]==12.*, but you have cuda-toolkit 13.0.2 which is incompatible.\ngcsfs 2025.3.0 requires fsspec==2025.3.0, but you have fsspec 2025.9.0 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0m Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m174.6/174.6 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m728.6/728.6 kB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m253.3/253.3 kB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m208.5/208.5 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m142.4/142.4 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.4/96.4 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m152.3/152.3 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.2/80.2 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25h✓ Dependencies installed\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"# remove these two lines:\n# import nest_asyncio\n# nest_asyncio.apply()\n\nimport json, re, time\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport httpx\nimport torch\nfrom unsloth import FastLanguageModel\nfrom trl import GRPOConfig, GRPOTrainer, SFTTrainer, SFTConfig\nfrom datasets import Dataset\n\nENV_URL = \"https://prothamd-prothamd-adaptive-world-env.hf.space\"\nMODEL_NAME = \"ProthamD/adaptive-world-grpo-qwen2.5-3b\"\n\nprint(f'✓ ENV_URL: {ENV_URL}')\nprint(f'✓ Model: {MODEL_NAME}')\nprint(f'✓ GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"CPU\"}')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-30T18:40:33.028408Z","iopub.execute_input":"2026-04-30T18:40:33.028719Z","iopub.status.idle":"2026-04-30T18:41:19.573720Z","shell.execute_reply.started":"2026-04-30T18:40:33.028686Z","shell.execute_reply":"2026-04-30T18:41:19.572778Z"}},"outputs":[{"name":"stdout","text":"🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n","output_type":"stream"},{"name":"stderr","text":"2026-04-30 18:40:41.880046: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1777574442.107449 57 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1777574442.169832 57 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nW0000 00:00:1777574442.669204 57 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1777574442.669238 57 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1777574442.669241 57 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1777574442.669243 57 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n","output_type":"stream"},{"name":"stdout","text":"🦥 Unsloth Zoo will now patch everything to make training faster!\n✓ ENV_URL: https://prothamd-prothamd-adaptive-world-env.hf.space\n✓ Model: ProthamD/adaptive-world-grpo-qwen2.5-3b\n✓ GPU: Tesla T4\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"def env_run_episode(actions: list, scenario_id=\"auto\", difficulty=\"easy\"):\n try:\n with httpx.Client(base_url=ENV_URL, timeout=25.0) as c:\n r = c.post(\"/run_episode\", json={\n \"scenario_id\": scenario_id,\n \"difficulty\": difficulty,\n \"actions\": actions,\n })\n r.raise_for_status()\n return r.json()\n except Exception:\n noise = np.random.uniform(-0.05, 0.05)\n return {\"task_reward\": 0.15 + noise, \"belief_accuracy\": abs(noise), \"reward\": 0.105 + noise}\n\ndef env_health():\n try:\n with httpx.Client(base_url=ENV_URL, timeout=10.0) as c:\n data = c.get(\"/health\").json()\n print(f\"✓ Health: {data}\")\n return True\n except Exception as e:\n print(f\"✗ {e}\")\n return False\n\nenv_health()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-30T18:41:49.185401Z","iopub.execute_input":"2026-04-30T18:41:49.186955Z","iopub.status.idle":"2026-04-30T18:41:49.525648Z","shell.execute_reply.started":"2026-04-30T18:41:49.186902Z","shell.execute_reply":"2026-04-30T18:41:49.525005Z"}},"outputs":[{"name":"stdout","text":"✓ Health: {'status': 'healthy'}\n","output_type":"stream"},{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":3},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"task_rewards_log = []\nbelief_accuracy_log = []\ncombined_rewards_log = []\ntraining_steps_log = []\nTRAINING_STEP = 0\n\nSYSTEM_PROMPT = \"\"\"You are an API agent. Your job is to complete tasks by calling APIs correctly.\n\nThe API world mutates silently — field names, endpoints, and auth schemes may change WITHOUT warning.\nYou will see the error history from previous calls. Use it to detect drift and adapt.\n\nSCORING (you are evaluated on BOTH independently):\n1. task_reward: did your corrected API call actually succeed?\n2. belief_accuracy: are your belief_state fields correct?\n\nOutput ONLY valid JSON. No markdown. No explanation.\n\nOUTPUT FORMAT:\n{\n \"action_type\": \"call_api\",\n \"method\": \"POST\",\n \"url\": \"/mock_api/orders\",\n \"body\": {\"quantity\": 2, \"product_id\": 5},\n \"belief_state\": {\n \"drift_detected\": true,\n \"order_field\": \"quantity\",\n \"endpoint\": \"/mock_api/orders\",\n \"max_rooms_per_request\": 1,\n \"bulk_booking_allowed\": false,\n \"what_changed\": \"field renamed from qty to quantity\"\n }\n}\"\"\"\n\nprint('✓ Logs and system prompt ready')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-30T18:41:52.029363Z","iopub.execute_input":"2026-04-30T18:41:52.029936Z","iopub.status.idle":"2026-04-30T18:41:52.035351Z","shell.execute_reply.started":"2026-04-30T18:41:52.029889Z","shell.execute_reply":"2026-04-30T18:41:52.034540Z"}},"outputs":[{"name":"stdout","text":"✓ Logs and system prompt ready\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"import gc\ngc.collect()\ntorch.cuda.empty_cache()\n\nmodel, tokenizer = FastLanguageModel.from_pretrained(\n model_name=MODEL_NAME,\n max_seq_length=2048, # FIX: was 1024\n dtype=None,\n load_in_4bit=True,\n)\n\nif tokenizer.pad_token is None:\n tokenizer.pad_token = tokenizer.eos_token\n\nmodel = 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=16, # FIX: was 32, keep conservative for SFT→GRPO\n lora_dropout=0.05,\n bias=\"none\",\n use_gradient_checkpointing=\"unsloth\",\n random_state=42,\n)\n\ntrainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\ntotal = sum(p.numel() for p in model.parameters())\nprint(f\"✓ Loaded: {MODEL_NAME}\")\nprint(f\" Trainable: {trainable/1e6:.1f}M / {total/1e9:.2f}B ({100*trainable/total:.2f}%)\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-30T18:41:55.767067Z","iopub.execute_input":"2026-04-30T18:41:55.767775Z","iopub.status.idle":"2026-04-30T18:42:51.362743Z","shell.execute_reply.started":"2026-04-30T18:41:55.767726Z","shell.execute_reply":"2026-04-30T18:42:51.361869Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/peft/config.py:220: UserWarning: Unexpected keyword arguments ['lora_ga_config', 'use_bdlora'] for class LoraConfig, these are ignored. This probably means that you're loading a configuration file that was saved using a higher version of the library and additional parameters have been introduced since. It is highly recommended to upgrade the PEFT version before continuing (e.g. by running `pip install -U peft`).\n warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"==((====))== Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 4.56.2.\n \\\\ /| Tesla T4. Num GPUs = 2. Max memory: 14.563 GB. Platform: Linux.\nO^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n\\ / Bfloat16 = FALSE. FA [Xformers = None. FA2 = False]\n \"-____-\" Free license: http://github.com/unslothai/unsloth\nUnsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"model.safetensors.index.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ef0ae349447b40b9bf8fe39ff601d7f0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00001-of-00002.safetensors: 0%| | 0.00/3.97G [00:00","text/html":"\n \n \n
\n [30/30 02:34, Epoch 0/1]\n
\n \n \n Step \n Training Loss \n \n \n \n \n 5 \n 1.648800 \n \n \n 10 \n 1.309400 \n \n \n 15 \n 0.988900 \n \n \n 20 \n 0.778900 \n \n \n 25 \n 0.693000 \n \n \n 30 \n 0.630200 \n \n \n
"},"metadata":{}},{"name":"stdout","text":"✓ SFT done — proceeding to GRPO\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"SCENARIO_PROMPTS = [\n \"Place an order for product_id 5, quantity 2. POST /mock_api/orders. The quantity field name may change after your first call.\",\n \"Book a standard room for 2 nights. POST /mock_api/rooms/book. The booking endpoint may version-bump mid-task.\",\n \"Search flights BOM to DEL departing tomorrow. GET /mock_api/flights/search. Auth scheme may change from Bearer to ApiKey.\",\n \"File insurance claim for policy_id 1234, amount 5000. POST /mock_api/claims. Both endpoint and field names may change.\",\n \"Apply discount code SAVE20. POST /mock_api/discount/apply. A membership_tier policy requirement may appear.\",\n \"Book 1 conference room for 2 nights. POST /mock_api/rooms/book. Rate limits or endpoint may change.\",\n \"Search for electronics products. GET /mock_api/products. Response keys may rename (products to items, price to cost).\",\n]\n\n\ndef parse_action(text):\n if isinstance(text, list):\n text = text[-1].get('content', '') if isinstance(text[-1], dict) else str(text[-1])\n text = str(text).strip()\n text = re.sub(r'```(?:json)?', '', text).strip().rstrip('`').strip()\n depth, start = 0, None\n for i, ch in enumerate(text):\n if ch == '{':\n if depth == 0: start = i\n depth += 1\n elif ch == '}':\n depth -= 1\n if depth == 0 and start is not None:\n try: return json.loads(text[start:i+1])\n except: pass\n return {\"action_type\": \"probe_schema\"}\n\n\nSCENARIO_TRUTH = {\n 0: {\"field_name\": \"quantity\", \"endpoint\": \"/mock_api/orders\"},\n 1: {\"field_name\": \"nights\", \"endpoint\": \"/mock_api/v2/rooms/book\"},\n 2: {\"field_name\": \"departure\", \"endpoint\": \"/mock_api/flights/search\"},\n 3: {\"field_name\": \"claim_amount\", \"endpoint\": \"/mock_api/claims/v2\"},\n 4: {\"field_name\": \"coupon_code\", \"endpoint\": \"/mock_api/discount/apply\"},\n 5: {\"field_name\": \"room_count\", \"endpoint\": \"/mock_api/rooms/book\"},\n 6: {\"field_name\": \"category\", \"endpoint\": \"/mock_api/products\"},\n}\n\n\ndef get_quantity(body: dict) -> int:\n for key in (\"quantity\", \"qty\", \"count\", \"amount\", \"num\", \"number\"):\n if key in body:\n val = body[key]\n if isinstance(val, (int, float)):\n return int(val)\n for k, v in body.items():\n if k not in (\"product_id\", \"customer_id\") and isinstance(v, (int, float)):\n return int(v)\n return 2\n\n\ndef build_action_sequence(first_action: dict, difficulty: str) -> list:\n pre_drift = {\"easy\": 2, \"medium\": 3, \"hard\": 3}.get(difficulty, 2)\n belief = first_action.get(\"belief_state\") or {}\n field = belief.get(\"order_field\") or belief.get(\"field_name\") or \"quantity\"\n ep = belief.get(\"endpoint\", first_action.get(\"url\", \"/mock_api/orders\"))\n method = first_action.get(\"method\", \"POST\")\n body = first_action.get(\"body\") or {}\n qty = get_quantity(body)\n\n corrected_body = {\n field: qty,\n \"product_id\": int(body.get(\"product_id\", 5)),\n \"customer_id\": str(body.get(\"customer_id\", \"c1\")),\n }\n\n actions = []\n for _ in range(pre_drift):\n actions.append(first_action)\n actions.append({\"action_type\": \"probe_schema\"})\n actions.append({\n \"action_type\": \"call_api\",\n \"method\": method,\n \"url\": ep,\n \"headers\": first_action.get(\"headers\") or {},\n \"body\": corrected_body,\n })\n # Forward the model's full belief (max_rooms_per_request, bulk_booking_allowed,\n # category, etc.) to the env scorer, but ensure corrected order_field/endpoint\n # are present so the env always sees them.\n final_belief = {**belief} if isinstance(belief, dict) else {}\n final_belief.setdefault(\"drift_detected\", True)\n final_belief[\"order_field\"] = field\n final_belief[\"endpoint\"] = ep\n final_belief.setdefault(\"what_changed\", \"field renamed\")\n actions.append({\n \"action_type\": \"submit_result\",\n \"belief_state\": final_belief,\n })\n return actions\n\n\ndef score_locally(action: dict, step: int) -> tuple:\n belief = action.get(\"belief_state\") or {}\n body = action.get(\"body\") or {}\n url = action.get(\"url\", \"\")\n method = action.get(\"method\", \"POST\")\n truth = SCENARIO_TRUTH[step % len(SCENARIO_TRUTH)]\n\n task_r = 0.0\n if action.get(\"action_type\") == \"call_api\":\n task_r += 0.10\n if url == truth[\"endpoint\"]:\n task_r += 0.40\n elif url.startswith(\"/mock_api\"):\n task_r += 0.10\n if truth[\"field_name\"] in body:\n task_r += 0.35\n if \"product_id\" in body:\n task_r += 0.05\n if \"customer_id\" in body:\n task_r += 0.05\n if method in (\"POST\", \"GET\"):\n task_r += 0.05\n elif action.get(\"action_type\") == \"probe_schema\":\n task_r = 0.05\n else:\n task_r = 0.0\n task_r = min(task_r, 1.0)\n\n belief_r = 0.0\n if not belief:\n belief_r = 0.0\n else:\n if (belief.get(\"order_field\") or belief.get(\"field_name\")) == truth[\"field_name\"]:\n belief_r += 0.50\n elif (belief.get(\"order_field\") or belief.get(\"field_name\")) in (\"quantity\",\"qty\",\"amount\",\"count\",\"nights\",\"departure\"):\n belief_r += 0.20\n if belief.get(\"endpoint\") == truth[\"endpoint\"]:\n belief_r += 0.35\n elif belief.get(\"endpoint\", \"\").startswith(\"/mock_api\"):\n belief_r += 0.10\n if belief.get(\"drift_detected\") is True:\n belief_r += 0.10\n if belief.get(\"what_changed\", \"\") not in (\"\", \"nothing yet\", \"unknown\"):\n belief_r += 0.05\n belief_r = min(belief_r, 1.0)\n\n return task_r, belief_r\n\n\ndef run_full_episode(completion: str, difficulty: str = \"easy\", step: int = 0) -> tuple:\n action = parse_action(completion)\n actions = build_action_sequence(action, difficulty)\n result = env_run_episode(actions, difficulty=difficulty)\n\n task_r = float(result.get(\"task_reward\", 0.0))\n belief_r = float(result.get(\"belief_accuracy\", 0.0))\n\n # If Space returned fallback, use local scoring\n if belief_r == 0.0 and 0.09 < task_r < 0.21:\n task_r, belief_r = score_locally(action, step)\n\n combined = float(np.clip(task_r * 0.7 + belief_r * 0.3, -1.0, 1.0))\n return task_r, belief_r, combined","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-30T18:46:14.925773Z","iopub.execute_input":"2026-04-30T18:46:14.926638Z","iopub.status.idle":"2026-04-30T18:46:14.946242Z","shell.execute_reply.started":"2026-04-30T18:46:14.926590Z","shell.execute_reply":"2026-04-30T18:46:14.945374Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"def build_prompt(task: str) -> str:\n messages = [\n {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n {\"role\": \"user\", \"content\": task},\n ]\n return tokenizer.apply_chat_template(\n messages,\n tokenize=False,\n add_generation_prompt=True,\n )\n\nREPEAT = 12 # 7 × 12 = 84 prompts\nprompts_dataset = Dataset.from_dict({\n \"prompt\": [build_prompt(p) for p in SCENARIO_PROMPTS * REPEAT],\n})\nprint(f\"✓ prompts_dataset: {len(prompts_dataset)} entries\")\nprint(\"Sample (first 200 chars):\")\nprint(prompts_dataset[0][\"prompt\"][:200])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-30T18:46:18.794511Z","iopub.execute_input":"2026-04-30T18:46:18.795007Z","iopub.status.idle":"2026-04-30T18:46:18.811661Z","shell.execute_reply.started":"2026-04-30T18:46:18.794960Z","shell.execute_reply":"2026-04-30T18:46:18.811039Z"}},"outputs":[{"name":"stdout","text":"✓ prompts_dataset: 84 entries\nSample (first 200 chars):\n<|im_start|>system\nYou are an API agent. Your job is to complete tasks by calling APIs correctly.\n\nThe API world mutates silently — field names, endpoints, and auth schemes may change WITHOUT warning.\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"\n# ── Shaped local scorer (always runs — never skipped) ──────────────────\ndef shaped_score(action: dict, scenario_idx: int):\n truth = SCENARIO_TRUTH[scenario_idx % len(SCENARIO_TRUTH)]\n body = action.get(\"body\") or {}\n url = action.get(\"url\", \"\")\n method = action.get(\"method\", \"\")\n atype = action.get(\"action_type\", \"\")\n belief = action.get(\"belief_state\") or {}\n\n task = 0.05\n if atype == \"call_api\":\n task += 0.10\n if method in (\"POST\", \"GET\", \"PUT\", \"DELETE\"): task += 0.10\n if url.startswith(\"/mock_api\"): task += 0.20\n if url == truth[\"endpoint\"]: task += 0.30\n if truth[\"field_name\"] in body: task += 0.35\n if \"product_id\" in body or \"customer_id\" in body: task += 0.05\n if url == truth[\"endpoint\"] and truth[\"field_name\"] in body:\n task += 0.10\n known = {truth[\"field_name\"], \"product_id\", \"customer_id\",\n \"nights\", \"departure\", \"amount\", \"coupon_code\",\n \"room_count\", \"category\"}\n extra = [k for k in body if k not in known]\n task -= 0.01 * min(len(extra), 3)\n elif atype == \"probe_schema\":\n task = 0.08\n\n bel = 0.0\n if belief:\n bel += 0.10\n if belief.get(\"drift_detected\") is True: bel += 0.10\n fname = belief.get(\"order_field\") or belief.get(\"field_name\") or \"\"\n if fname == truth[\"field_name\"]: bel += 0.40\n elif fname in (\"quantity\",\"qty\",\"amount\",\"count\",\n \"nights\",\"departure\",\"coupon_code\",\n \"room_count\",\"category\"): bel += 0.15\n ep = belief.get(\"endpoint\", \"\")\n if ep == truth[\"endpoint\"]: bel += 0.30\n elif ep.startswith(\"/mock_api\"): bel += 0.10\n if belief.get(\"what_changed\",\"\") not in (\"\",\"nothing yet\",\"unknown\"):\n bel += 0.10\n if fname == truth[\"field_name\"] and ep == truth[\"endpoint\"]:\n bel += 0.10\n\n return float(np.clip(task, 0.0, 1.0)), float(np.clip(bel, 0.0, 1.0))\n\n# ── Curriculum state ────────────────────────────────────────────────────\n_reward_call_count = 0\n_GRAD_STEPS = 0\n_DIFFICULTY = \"easy\"\nrollout_buffer = []\n\n# ── Scenario detection from prompt (matches actual scenario, not rotation) ──\ndef detect_scenario_from_prompt(prompt_text) -> int:\n \"\"\"Return the SCENARIO_TRUTH index based on unique markers in the user task.\n Falls back to 0 if no marker matches.\"\"\"\n if isinstance(prompt_text, list):\n prompt_text = prompt_text[-1].get(\"content\", \"\") if isinstance(prompt_text[-1], dict) else str(prompt_text[-1])\n p = prompt_text if isinstance(prompt_text, str) else \"\"\n if \"Place an order\" in p: return 0\n if \"standard room\" in p: return 1\n if \"BOM to DEL\" in p: return 2\n if \"policy_id\" in p: return 3\n if \"SAVE20\" in p: return 4\n if \"conference room\" in p: return 5\n if \"electronics products\" in p: return 6\n return 0\n\n# ── Single reward_fn ────────────────────────────────────────────────────\ndef reward_fn(completions, prompts=None, **kwargs):\n global _reward_call_count\n _reward_call_count += 1\n difficulty = _DIFFICULTY\n rewards, ts, bs = [], [], []\n\n for i, comp in enumerate(completions):\n # Detect scenario from the actual prompt the model is responding to,\n # so shaped_score checks against the correct truth (not a rotating counter).\n prompt_text = prompts[i] if prompts is not None and i < len(prompts) else \"\"\n scenario_idx = detect_scenario_from_prompt(prompt_text)\n action = parse_action(comp)\n\n t_local, b_local = shaped_score(action, scenario_idx)\n t, b = t_local, b_local\n\n try:\n acts = build_action_sequence(action, difficulty)\n result = env_run_episode(acts, difficulty=difficulty)\n t_raw = float(result.get(\"task_reward\", 0.0))\n b_raw = float(result.get(\"belief_accuracy\", 0.0))\n # Blend only when env call succeeds — no heuristic needed.\n # Removed broken env_is_real check (abs(noise) never ==0.0).\n t = 0.6 * t_local + 0.4 * t_raw\n b = 0.6 * b_local + 0.4 * b_raw\n except Exception:\n # Env unreachable — keep local score only, no noise blended in\n pass\n\n difficulty_bonus = {\"easy\": 0.0, \"medium\": 0.05, \"hard\": 0.10}[difficulty]\n combined = float(np.clip(t * 0.65 + b * 0.35 + difficulty_bonus, 0.0, 1.0))\n\n task_rewards_log.append(t)\n belief_accuracy_log.append(b)\n combined_rewards_log.append(combined)\n training_steps_log.append(_GRAD_STEPS)\n ts.append(t); bs.append(b); rewards.append(combined)\n\n prompt_text = prompts[i] if prompts is not None and i < len(prompts) else \"\"\n rollout_buffer.append({\n \"prompt\": prompt_text,\n \"completion\": comp if isinstance(comp, str) else str(comp),\n \"task_reward\": t,\n \"belief_accuracy\": b,\n \"combined_reward\": combined,\n \"difficulty\": difficulty,\n \"grad_step\": _GRAD_STEPS,\n })\n\n if _reward_call_count % 5 == 0:\n print(f\" [call {_reward_call_count:3d} | step {_GRAD_STEPS:3d} | {difficulty}] \"\n f\"task={np.mean(ts):.3f} belief={np.mean(bs):.3f} combined={np.mean(rewards):.3f}\")\n return rewards\n\nprint(\"✓ reward_fn ready (shaped blend, higher local trust)\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-30T18:46:24.641202Z","iopub.execute_input":"2026-04-30T18:46:24.641550Z","iopub.status.idle":"2026-04-30T18:46:24.658761Z","shell.execute_reply.started":"2026-04-30T18:46:24.641510Z","shell.execute_reply":"2026-04-30T18:46:24.657882Z"}},"outputs":[{"name":"stdout","text":"✓ reward_fn ready (shaped blend, higher local trust)\n","output_type":"stream"}],"execution_count":10},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"\n\nfrom transformers import TrainerCallback\n\nclass RewardLoggerCallback(TrainerCallback):\n def on_log(self, args, state, control, logs=None, **kwargs):\n if not logs or not task_rewards_log: return\n logs[\"task_reward\"] = round(np.mean(task_rewards_log[-4:]), 4)\n logs[\"belief_accuracy\"] = round(np.mean(belief_accuracy_log[-4:]), 4)\n logs[\"combined_reward\"] = round(np.mean(combined_rewards_log[-4:]),4)\n\nclass CurriculumCallback(TrainerCallback):\n def on_step_end(self, args, state, control, **kwargs):\n global _GRAD_STEPS, _DIFFICULTY\n _GRAD_STEPS = state.global_step\n if _GRAD_STEPS < 50: _DIFFICULTY = \"easy\"\n elif _GRAD_STEPS < 110: _DIFFICULTY = \"medium\"\n else: _DIFFICULTY = \"hard\"\n\ntraining_args = GRPOConfig(\n temperature=0.9,\n learning_rate=2e-6,\n weight_decay=0.01,\n warmup_ratio=0.1,\n lr_scheduler_type=\"cosine\",\n optim=\"adamw_8bit\",\n max_grad_norm=0.2,\n logging_steps=1,\n output_dir=\"adaptive-world-grpo\",\n per_device_train_batch_size=1,\n gradient_accumulation_steps=4,\n num_generations=4,\n max_prompt_length=1024,\n max_completion_length=400,\n max_steps=200,\n save_steps=25,\n fp16=True,\n dataloader_num_workers=0,\n remove_unused_columns=False,\n)\n\ntrainer = GRPOTrainer(\n model=model,\n processing_class=tokenizer,\n reward_funcs=reward_fn,\n args=training_args,\n train_dataset=prompts_dataset,\n callbacks=[RewardLoggerCallback(), CurriculumCallback()],\n)\n\nprint(\"Resuming GRPO from checkpoint-50...\")\nprint(\"-\" * 60)\ntrainer.train()\nprint(\"-\" * 60)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-30T17:49:18.490216Z","iopub.execute_input":"2026-04-30T17:49:18.490527Z","iopub.status.idle":"2026-04-30T17:49:18.585206Z","shell.execute_reply.started":"2026-04-30T17:49:18.490504Z","shell.execute_reply":"2026-04-30T17:49:18.584219Z"}},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":" Step Training Loss reward reward_std completions / mean_length \\\n0 1 0.0000 0.438560 0.000000 181.75 \n1 2 0.0002 0.239283 0.140017 148.00 \n2 3 0.0000 0.828506 0.152164 169.75 \n3 4 0.0001 0.676342 0.152164 106.00 \n4 5 0.0000 0.463845 0.050569 162.00 \n5 6 0.0001 0.676342 0.152164 123.75 \n6 7 0.0001 0.455604 0.124439 136.50 \n7 8 0.0001 0.413842 0.052068 164.50 \n8 9 0.0001 0.401810 0.010500 142.50 \n9 10 0.0003 0.222367 0.133778 150.50 \n10 11 0.0001 0.438152 0.081715 142.75 \n11 12 0.0002 0.107542 0.152164 199.50 \n12 13 0.0001 0.418726 0.059235 254.00 \n13 14 0.0002 0.283717 0.037757 131.75 \n14 15 0.0001 0.297410 0.179770 162.00 \n15 16 0.0001 0.275508 0.177936 226.00 \n16 17 0.0001 0.295342 0.179101 173.75 \n17 18 0.0001 0.752424 0.175704 142.25 \n18 19 0.0001 0.531888 0.066794 179.00 \n19 20 0.0001 0.389850 0.075231 202.25 \n20 21 0.0000 0.422520 0.078735 145.00 \n21 22 0.0001 0.291151 0.032208 149.00 \n22 23 0.0001 0.491764 0.049100 150.00 \n23 24 0.0000 0.351954 0.215680 179.75 \n24 25 0.0000 0.436135 0.110243 146.75 \n25 26 0.0001 0.501840 0.187630 163.50 \n26 27 0.0001 0.441485 0.001950 133.50 \n27 28 0.0001 0.330710 0.097117 137.00 \n28 29 0.0005 0.425175 0.028599 131.25 \n29 30 0.0002 0.676342 0.152164 186.00 \n30 31 0.0001 0.752424 0.175704 192.50 \n31 32 0.0002 0.442091 0.026940 115.00 \n32 33 0.0001 0.407767 0.194260 164.25 \n33 34 0.0001 0.449176 0.153489 194.25 \n34 35 0.0001 0.438566 0.015554 152.50 \n35 36 0.0001 0.330667 0.250889 149.25 \n36 37 0.0001 0.434510 0.018187 137.00 \n37 38 0.0001 0.209379 0.145371 136.75 \n38 39 0.0001 0.492002 0.147413 136.25 \n39 40 0.0000 0.266145 0.086264 149.00 \n40 41 0.0001 0.399579 0.103793 148.00 \n41 42 0.0002 0.574010 0.052500 116.25 \n42 43 0.0001 0.473594 0.046669 140.75 \n43 44 0.0001 0.366685 0.115896 136.75 \n44 45 0.0001 0.461601 0.051770 172.25 \n45 46 0.0001 0.326551 0.212770 238.00 \n46 47 0.0001 0.411985 0.107057 141.75 \n47 48 0.0004 0.421351 0.072040 141.25 \n48 49 0.0001 0.449966 0.021998 173.75 \n49 50 0.0001 0.456569 0.043482 141.50 \n50 51 0.0001 0.436710 0.044267 194.50 \n51 52 0.0001 0.696929 0.053889 158.50 \n52 53 0.0001 0.491485 0.003734 163.75 \n53 54 0.0001 0.412069 0.224425 127.25 \n54 55 0.0001 0.524390 0.299033 142.00 \n55 56 0.0001 0.548220 0.114612 150.00 \n56 57 0.0001 0.462801 0.090781 177.25 \n57 58 0.0000 0.650260 0.000000 126.25 \n58 59 0.0001 0.348786 0.123235 145.75 \n59 60 0.0000 0.285985 0.236171 204.50 \n60 61 0.0001 0.302995 0.178574 203.50 \n61 62 0.0001 0.468190 0.110350 204.75 \n62 63 0.0001 0.472180 0.282885 151.25 \n63 64 0.0001 0.539054 0.046605 190.75 \n64 65 0.0001 0.525639 0.095086 115.50 \n65 66 0.0002 0.409845 0.077256 139.25 \n66 67 0.0001 0.559505 0.081584 139.50 \n67 68 0.0000 0.673594 0.046669 160.50 \n68 69 0.0001 0.482635 0.014566 195.75 \n69 70 0.0001 0.438290 0.095949 141.50 \n70 71 0.0001 0.373385 0.121285 240.00 \n71 72 0.0001 0.328885 0.105130 151.00 \n72 73 0.0001 0.370761 0.101115 155.00 \n73 74 0.0000 0.696929 0.053889 131.25 \n74 75 0.0001 0.464020 0.092949 144.50 \n75 76 0.0000 0.696929 0.053889 132.75 \n76 77 0.0000 0.450160 0.079421 149.00 \n77 78 0.0000 0.464935 0.015750 151.75 \n78 79 0.0001 0.511635 0.038852 182.25 \n79 80 0.0001 0.476345 0.064423 182.50 \n80 81 0.0001 0.379810 0.199454 188.50 \n81 82 0.0001 0.442514 0.052545 127.75 \n82 83 0.0001 0.387320 0.116936 159.75 \n83 84 0.0000 0.483319 0.045058 176.25 \n84 85 0.0001 0.483020 0.049832 187.25 \n85 86 0.0002 0.430705 0.139750 129.75 \n86 87 0.0001 0.484510 0.018187 137.25 \n87 88 0.0000 0.650260 0.000000 110.00 \n88 89 0.0002 0.673594 0.046669 124.50 \n89 90 0.0000 0.371035 0.193620 208.50 \n90 91 0.0000 0.464935 0.015750 147.50 \n91 92 0.0001 0.316735 0.182232 131.25 \n92 93 0.0000 0.375995 0.204340 153.25 \n93 94 0.0000 0.673594 0.046669 110.00 \n94 95 0.0000 0.304260 0.257972 228.00 \n95 96 0.0001 0.376135 0.142429 141.75 \n96 97 0.0001 0.372245 0.210491 137.00 \n97 98 0.0000 0.673594 0.046669 110.00 \n98 99 0.0000 0.743598 0.000000 111.00 \n99 100 0.0001 0.501704 0.118444 137.50 \n100 101 0.0001 0.488270 0.044000 175.50 \n101 102 0.0000 0.673594 0.046669 110.00 \n102 103 0.0000 0.454060 0.076866 150.75 \n103 104 0.0000 0.436060 0.029699 162.50 \n104 105 0.0000 0.481369 0.046056 154.50 \n105 106 0.0001 0.451415 0.109445 160.25 \n106 107 0.0001 0.581864 0.090225 190.75 \n107 108 0.0000 0.464935 0.015750 154.50 \n108 109 0.0001 0.474469 0.103689 202.25 \n109 110 0.0001 0.413330 0.248198 180.50 \n110 111 0.0004 0.496455 0.218250 169.50 \n111 112 0.0001 0.495780 0.085686 155.00 \n112 113 0.0001 0.668440 0.102274 128.75 \n113 114 0.0000 0.751945 0.008630 110.00 \n114 115 0.0000 0.739000 0.000000 110.00 \n115 116 0.0001 0.665815 0.115322 130.00 \n116 117 0.0001 0.437015 0.138904 156.75 \n117 118 0.0001 0.336590 0.198202 147.00 \n118 119 0.0001 0.634130 0.161586 137.75 \n119 120 0.0001 0.428880 0.010216 187.75 \n120 121 0.0002 0.625250 0.115459 123.75 \n121 122 0.0000 0.403345 0.056279 143.00 \n122 123 0.0001 0.637730 0.064840 208.50 \n123 124 0.0001 0.464500 0.123319 120.50 \n124 125 0.0001 0.543215 0.082879 144.00 \n125 126 0.0001 0.692695 0.115909 114.00 \n126 127 0.0001 0.673690 0.107866 105.00 \n127 128 0.0001 0.517715 0.060186 160.75 \n128 129 0.0001 0.521995 0.068845 163.75 \n129 130 0.0000 0.588850 0.066772 183.50 \n130 131 0.0001 0.490190 0.101280 199.25 \n131 132 0.0001 0.644815 0.128879 164.00 \n132 133 0.0001 0.681565 0.115968 165.00 \n133 134 0.0001 0.679015 0.129510 140.25 \n134 135 0.0001 0.464955 0.076862 128.00 \n135 136 0.0000 0.743315 0.008630 110.00 \n136 137 0.0001 0.517345 0.103109 144.25 \n137 138 0.0001 0.603030 0.036726 244.75 \n138 139 0.0001 0.735175 0.108050 140.00 \n139 140 0.0001 0.658575 0.100384 139.75 \n140 141 0.0001 0.434540 0.188699 200.00 \n141 142 0.0000 0.739000 0.000000 110.00 \n142 143 0.0001 0.414665 0.024217 143.25 \n143 144 0.0001 0.579250 0.002252 171.75 \n144 145 0.0001 0.411105 0.012889 181.50 \n145 146 0.0001 0.422540 0.020326 138.00 \n146 147 0.0002 0.635670 0.110090 153.50 \n147 148 0.0001 0.621690 0.096735 253.50 \n148 149 0.0001 0.435740 0.186803 191.00 \n149 150 0.0002 0.439225 0.066061 129.00 \n150 151 0.0001 0.588220 0.016849 154.75 \n151 152 0.0001 0.747630 0.009965 105.75 \n152 153 0.0001 0.544645 0.074975 170.75 \n153 154 0.0001 0.389350 0.054560 158.75 \n154 155 0.0001 0.404125 0.024712 171.00 \n155 156 0.0000 0.303815 0.102252 216.25 \n156 157 0.0001 0.415420 0.020326 140.50 \n157 158 0.0000 0.743315 0.008630 110.00 \n158 159 0.0000 0.437125 0.193180 143.50 \n159 160 0.0000 0.743315 0.008630 110.00 \n160 161 0.0001 0.577640 0.007800 139.50 \n161 162 0.0001 0.551090 0.008189 179.25 \n162 163 0.0001 0.629565 0.113965 146.50 \n163 164 0.0001 0.626005 0.095162 132.75 \n164 165 0.0001 0.685125 0.111738 218.00 \n165 166 0.0001 0.412715 0.021271 197.00 \n166 167 0.0001 0.499380 0.208609 210.75 \n167 168 0.0001 0.545800 0.000000 152.00 \n168 169 0.0001 0.446650 0.184566 172.25 \n169 170 0.0002 0.410350 0.018187 149.50 \n170 171 0.0001 0.681565 0.097176 138.25 \n171 172 0.0000 0.743315 0.008630 110.00 \n172 173 0.0000 0.751945 0.008630 110.00 \n173 174 0.0000 0.565865 0.024217 148.75 \n174 175 0.0001 0.542240 0.004951 173.25 \n175 176 0.0001 0.417445 0.015859 145.00 \n176 177 0.0000 0.534365 0.034417 171.75 \n177 178 0.0000 0.743315 0.008630 110.00 \n178 179 0.0000 0.569425 0.015750 156.75 \n179 180 0.0001 0.561405 0.086123 139.75 \n180 181 0.0001 0.586490 0.007253 175.50 \n181 182 0.0000 0.739000 0.000000 110.00 \n182 183 0.0000 0.743315 0.008630 111.00 \n183 184 0.0000 0.343980 0.210234 182.25 \n184 185 0.0001 0.420130 0.212945 177.25 \n185 186 0.0001 0.681565 0.097176 212.50 \n186 187 0.0001 0.493375 0.082201 135.25 \n187 188 0.0001 0.481790 0.196229 168.75 \n188 189 0.0001 0.500605 0.101832 157.50 \n189 190 0.0001 0.513180 0.100658 184.75 \n190 191 0.0003 0.626080 0.115140 155.75 \n191 192 0.0001 0.504240 0.241419 146.75 \n192 193 0.0001 0.679015 0.104637 159.75 \n193 194 0.0002 0.409415 0.031267 188.00 \n194 195 0.0001 0.731575 0.089468 214.25 \n195 196 0.0001 0.505975 0.085279 133.00 \n196 197 0.0001 0.574080 0.020738 151.75 \n197 198 0.0001 0.665815 0.115322 142.25 \n198 199 0.0001 0.625250 0.094672 205.50 \n199 200 0.0001 0.398875 0.033887 194.75 \n\n completions / min_length completions / max_length \\\n0 143.0 256.0 \n1 119.0 184.0 \n2 110.0 349.0 \n3 94.0 110.0 \n4 146.0 188.0 \n5 110.0 139.0 \n6 119.0 152.0 \n7 125.0 203.0 \n8 135.0 147.0 \n9 134.0 169.0 \n10 116.0 168.0 \n11 117.0 400.0 \n12 109.0 400.0 \n13 118.0 141.0 \n14 110.0 205.0 \n15 158.0 400.0 \n16 142.0 206.0 \n17 93.0 190.0 \n18 150.0 207.0 \n19 149.0 299.0 \n20 133.0 157.0 \n21 126.0 165.0 \n22 125.0 178.0 \n23 140.0 250.0 \n24 132.0 172.0 \n25 132.0 227.0 \n26 117.0 158.0 \n27 126.0 151.0 \n28 105.0 147.0 \n29 114.0 311.0 \n30 93.0 362.0 \n31 81.0 138.0 \n32 128.0 233.0 \n33 142.0 277.0 \n34 128.0 168.0 \n35 134.0 179.0 \n36 116.0 169.0 \n37 123.0 150.0 \n38 122.0 157.0 \n39 140.0 154.0 \n40 132.0 171.0 \n41 88.0 191.0 \n42 109.0 175.0 \n43 134.0 143.0 \n44 134.0 220.0 \n45 147.0 400.0 \n46 132.0 148.0 \n47 21.0 233.0 \n48 103.0 228.0 \n49 123.0 170.0 \n50 126.0 254.0 \n51 93.0 302.0 \n52 136.0 212.0 \n53 97.0 142.0 \n54 120.0 177.0 \n55 134.0 158.0 \n56 132.0 264.0 \n57 110.0 146.0 \n58 102.0 205.0 \n59 134.0 400.0 \n60 126.0 400.0 \n61 132.0 400.0 \n62 134.0 187.0 \n63 105.0 400.0 \n64 101.0 136.0 \n65 135.0 147.0 \n66 111.0 179.0 \n67 110.0 206.0 \n68 140.0 285.0 \n69 82.0 212.0 \n70 160.0 395.0 \n71 124.0 178.0 \n72 128.0 213.0 \n73 110.0 195.0 \n74 124.0 173.0 \n75 110.0 182.0 \n76 140.0 154.0 \n77 134.0 189.0 \n78 121.0 242.0 \n79 107.0 400.0 \n80 103.0 400.0 \n81 107.0 143.0 \n82 127.0 233.0 \n83 170.0 184.0 \n84 137.0 291.0 \n85 111.0 145.0 \n86 103.0 192.0 \n87 110.0 110.0 \n88 110.0 144.0 \n89 141.0 400.0 \n90 141.0 156.0 \n91 124.0 137.0 \n92 136.0 165.0 \n93 110.0 110.0 \n94 119.0 400.0 \n95 132.0 151.0 \n96 110.0 160.0 \n97 110.0 110.0 \n98 110.0 114.0 \n99 125.0 145.0 \n100 143.0 223.0 \n101 110.0 110.0 \n102 142.0 169.0 \n103 126.0 258.0 \n104 142.0 169.0 \n105 126.0 188.0 \n106 139.0 289.0 \n107 142.0 170.0 \n108 144.0 270.0 \n109 127.0 300.0 \n110 129.0 264.0 \n111 118.0 199.0 \n112 98.0 154.0 \n113 110.0 110.0 \n114 110.0 110.0 \n115 103.0 177.0 \n116 125.0 210.0 \n117 116.0 168.0 \n118 116.0 172.0 \n119 144.0 233.0 \n120 82.0 159.0 \n121 137.0 153.0 \n122 113.0 400.0 \n123 65.0 164.0 \n124 130.0 164.0 \n125 110.0 126.0 \n126 98.0 116.0 \n127 135.0 191.0 \n128 123.0 203.0 \n129 142.0 265.0 \n130 127.0 328.0 \n131 101.0 207.0 \n132 112.0 188.0 \n133 117.0 166.0 \n134 113.0 146.0 \n135 110.0 110.0 \n136 132.0 156.0 \n137 169.0 400.0 \n138 126.0 147.0 \n139 126.0 165.0 \n140 134.0 370.0 \n141 110.0 110.0 \n142 137.0 154.0 \n143 153.0 203.0 \n144 126.0 329.0 \n145 117.0 168.0 \n146 124.0 186.0 \n147 100.0 400.0 \n148 112.0 400.0 \n149 120.0 143.0 \n150 133.0 180.0 \n151 93.0 110.0 \n152 130.0 200.0 \n153 120.0 222.0 \n154 125.0 287.0 \n155 143.0 400.0 \n156 125.0 163.0 \n157 110.0 110.0 \n158 124.0 160.0 \n159 110.0 110.0 \n160 122.0 165.0 \n161 135.0 237.0 \n162 119.0 163.0 \n163 109.0 178.0 \n164 130.0 354.0 \n165 162.0 250.0 \n166 136.0 400.0 \n167 144.0 161.0 \n168 128.0 238.0 \n169 133.0 165.0 \n170 102.0 158.0 \n171 110.0 110.0 \n172 110.0 110.0 \n173 143.0 153.0 \n174 137.0 244.0 \n175 135.0 161.0 \n176 113.0 245.0 \n177 110.0 110.0 \n178 137.0 200.0 \n179 126.0 162.0 \n180 152.0 223.0 \n181 110.0 110.0 \n182 110.0 114.0 \n183 112.0 334.0 \n184 133.0 253.0 \n185 106.0 392.0 \n186 114.0 169.0 \n187 152.0 203.0 \n188 110.0 196.0 \n189 122.0 252.0 \n190 137.0 193.0 \n191 128.0 180.0 \n192 116.0 208.0 \n193 129.0 346.0 \n194 117.0 400.0 \n195 110.0 146.0 \n196 142.0 160.0 \n197 113.0 178.0 \n198 118.0 337.0 \n199 92.0 400.0 \n\n completions / clipped_ratio completions / mean_terminated_length \\\n0 0.00 181.750000 \n1 0.00 148.000000 \n2 0.00 169.750000 \n3 0.00 106.000000 \n4 0.00 162.000000 \n5 0.00 123.750000 \n6 0.00 136.500000 \n7 0.00 164.500000 \n8 0.00 142.500000 \n9 0.00 150.500000 \n10 0.00 142.750000 \n11 0.25 132.666672 \n12 0.25 205.333344 \n13 0.00 131.750000 \n14 0.00 162.000000 \n15 0.25 168.000000 \n16 0.00 173.750000 \n17 0.00 142.250000 \n18 0.00 179.000000 \n19 0.00 202.250000 \n20 0.00 145.000000 \n21 0.00 149.000000 \n22 0.00 150.000000 \n23 0.00 179.750000 \n24 0.00 146.750000 \n25 0.00 163.500000 \n26 0.00 133.500000 \n27 0.00 137.000000 \n28 0.00 131.250000 \n29 0.00 186.000000 \n30 0.00 192.500000 \n31 0.00 115.000000 \n32 0.00 164.250000 \n33 0.00 194.250000 \n34 0.00 152.500000 \n35 0.00 149.250000 \n36 0.00 137.000000 \n37 0.00 136.750000 \n38 0.00 136.250000 \n39 0.00 149.000000 \n40 0.00 148.000000 \n41 0.00 116.250000 \n42 0.00 140.750000 \n43 0.00 136.750000 \n44 0.00 172.250000 \n45 0.25 184.000000 \n46 0.00 141.750000 \n47 0.00 141.250000 \n48 0.00 173.750000 \n49 0.00 141.500000 \n50 0.00 194.500000 \n51 0.00 158.500000 \n52 0.00 163.750000 \n53 0.00 127.250000 \n54 0.00 142.000000 \n55 0.00 150.000000 \n56 0.00 177.250000 \n57 0.00 126.250000 \n58 0.00 145.750000 \n59 0.25 139.333344 \n60 0.25 138.000000 \n61 0.25 139.666672 \n62 0.00 151.250000 \n63 0.25 121.000000 \n64 0.00 115.500000 \n65 0.00 139.250000 \n66 0.00 139.500000 \n67 0.00 160.500000 \n68 0.00 195.750000 \n69 0.00 141.500000 \n70 0.00 240.000000 \n71 0.00 151.000000 \n72 0.00 155.000000 \n73 0.00 131.250000 \n74 0.00 144.500000 \n75 0.00 132.750000 \n76 0.00 149.000000 \n77 0.00 151.750000 \n78 0.00 182.250000 \n79 0.25 110.000000 \n80 0.25 118.000000 \n81 0.00 127.750000 \n82 0.00 159.750000 \n83 0.00 176.250000 \n84 0.00 187.250000 \n85 0.00 129.750000 \n86 0.00 137.250000 \n87 0.00 110.000000 \n88 0.00 124.500000 \n89 0.25 144.666672 \n90 0.00 147.500000 \n91 0.00 131.250000 \n92 0.00 153.250000 \n93 0.00 110.000000 \n94 0.25 170.666672 \n95 0.00 141.750000 \n96 0.00 137.000000 \n97 0.00 110.000000 \n98 0.00 111.000000 \n99 0.00 137.500000 \n100 0.00 175.500000 \n101 0.00 110.000000 \n102 0.00 150.750000 \n103 0.00 162.500000 \n104 0.00 154.500000 \n105 0.00 160.250000 \n106 0.00 190.750000 \n107 0.00 154.500000 \n108 0.00 202.250000 \n109 0.00 180.500000 \n110 0.00 169.500000 \n111 0.00 155.000000 \n112 0.00 128.750000 \n113 0.00 110.000000 \n114 0.00 110.000000 \n115 0.00 130.000000 \n116 0.00 156.750000 \n117 0.00 147.000000 \n118 0.00 137.750000 \n119 0.00 187.750000 \n120 0.00 123.750000 \n121 0.00 143.000000 \n122 0.25 144.666672 \n123 0.00 120.500000 \n124 0.00 144.000000 \n125 0.00 114.000000 \n126 0.00 105.000000 \n127 0.00 160.750000 \n128 0.00 163.750000 \n129 0.00 183.500000 \n130 0.00 199.250000 \n131 0.00 164.000000 \n132 0.00 165.000000 \n133 0.00 140.250000 \n134 0.00 128.000000 \n135 0.00 110.000000 \n136 0.00 144.250000 \n137 0.25 193.000000 \n138 0.00 140.000000 \n139 0.00 139.750000 \n140 0.00 200.000000 \n141 0.00 110.000000 \n142 0.00 143.250000 \n143 0.00 171.750000 \n144 0.00 181.500000 \n145 0.00 138.000000 \n146 0.00 153.500000 \n147 0.50 107.000000 \n148 0.25 121.333336 \n149 0.00 129.000000 \n150 0.00 154.750000 \n151 0.00 105.750000 \n152 0.00 170.750000 \n153 0.00 158.750000 \n154 0.00 171.000000 \n155 0.25 155.000000 \n156 0.00 140.500000 \n157 0.00 110.000000 \n158 0.00 143.500000 \n159 0.00 110.000000 \n160 0.00 139.500000 \n161 0.00 179.250000 \n162 0.00 146.500000 \n163 0.00 132.750000 \n164 0.00 218.000000 \n165 0.00 197.000000 \n166 0.25 147.666672 \n167 0.00 152.000000 \n168 0.00 172.250000 \n169 0.00 149.500000 \n170 0.00 138.250000 \n171 0.00 110.000000 \n172 0.00 110.000000 \n173 0.00 148.750000 \n174 0.00 173.250000 \n175 0.00 145.000000 \n176 0.00 171.750000 \n177 0.00 110.000000 \n178 0.00 156.750000 \n179 0.00 139.750000 \n180 0.00 175.500000 \n181 0.00 110.000000 \n182 0.00 111.000000 \n183 0.00 182.250000 \n184 0.00 177.250000 \n185 0.00 212.500000 \n186 0.00 135.250000 \n187 0.00 168.750000 \n188 0.00 157.500000 \n189 0.00 184.750000 \n190 0.00 155.750000 \n191 0.00 146.750000 \n192 0.00 159.750000 \n193 0.00 188.000000 \n194 0.25 152.333344 \n195 0.00 133.000000 \n196 0.00 151.750000 \n197 0.00 142.250000 \n198 0.00 205.500000 \n199 0.25 126.333336 \n\n completions / min_terminated_length completions / max_terminated_length \\\n0 143.0 256.0 \n1 119.0 184.0 \n2 110.0 349.0 \n3 94.0 110.0 \n4 146.0 188.0 \n5 110.0 139.0 \n6 119.0 152.0 \n7 125.0 203.0 \n8 135.0 147.0 \n9 134.0 169.0 \n10 116.0 168.0 \n11 117.0 148.0 \n12 109.0 360.0 \n13 118.0 141.0 \n14 110.0 205.0 \n15 158.0 181.0 \n16 142.0 206.0 \n17 93.0 190.0 \n18 150.0 207.0 \n19 149.0 299.0 \n20 133.0 157.0 \n21 126.0 165.0 \n22 125.0 178.0 \n23 140.0 250.0 \n24 132.0 172.0 \n25 132.0 227.0 \n26 117.0 158.0 \n27 126.0 151.0 \n28 105.0 147.0 \n29 114.0 311.0 \n30 93.0 362.0 \n31 81.0 138.0 \n32 128.0 233.0 \n33 142.0 277.0 \n34 128.0 168.0 \n35 134.0 179.0 \n36 116.0 169.0 \n37 123.0 150.0 \n38 122.0 157.0 \n39 140.0 154.0 \n40 132.0 171.0 \n41 88.0 191.0 \n42 109.0 175.0 \n43 134.0 143.0 \n44 134.0 220.0 \n45 147.0 257.0 \n46 132.0 148.0 \n47 21.0 233.0 \n48 103.0 228.0 \n49 123.0 170.0 \n50 126.0 254.0 \n51 93.0 302.0 \n52 136.0 212.0 \n53 97.0 142.0 \n54 120.0 177.0 \n55 134.0 158.0 \n56 132.0 264.0 \n57 110.0 146.0 \n58 102.0 205.0 \n59 134.0 146.0 \n60 126.0 153.0 \n61 132.0 153.0 \n62 134.0 187.0 \n63 105.0 130.0 \n64 101.0 136.0 \n65 135.0 147.0 \n66 111.0 179.0 \n67 110.0 206.0 \n68 140.0 285.0 \n69 82.0 212.0 \n70 160.0 395.0 \n71 124.0 178.0 \n72 128.0 213.0 \n73 110.0 195.0 \n74 124.0 173.0 \n75 110.0 182.0 \n76 140.0 154.0 \n77 134.0 189.0 \n78 121.0 242.0 \n79 107.0 113.0 \n80 103.0 144.0 \n81 107.0 143.0 \n82 127.0 233.0 \n83 170.0 184.0 \n84 137.0 291.0 \n85 111.0 145.0 \n86 103.0 192.0 \n87 110.0 110.0 \n88 110.0 144.0 \n89 141.0 149.0 \n90 141.0 156.0 \n91 124.0 137.0 \n92 136.0 165.0 \n93 110.0 110.0 \n94 119.0 236.0 \n95 132.0 151.0 \n96 110.0 160.0 \n97 110.0 110.0 \n98 110.0 114.0 \n99 125.0 145.0 \n100 143.0 223.0 \n101 110.0 110.0 \n102 142.0 169.0 \n103 126.0 258.0 \n104 142.0 169.0 \n105 126.0 188.0 \n106 139.0 289.0 \n107 142.0 170.0 \n108 144.0 270.0 \n109 127.0 300.0 \n110 129.0 264.0 \n111 118.0 199.0 \n112 98.0 154.0 \n113 110.0 110.0 \n114 110.0 110.0 \n115 103.0 177.0 \n116 125.0 210.0 \n117 116.0 168.0 \n118 116.0 172.0 \n119 144.0 233.0 \n120 82.0 159.0 \n121 137.0 153.0 \n122 113.0 182.0 \n123 65.0 164.0 \n124 130.0 164.0 \n125 110.0 126.0 \n126 98.0 116.0 \n127 135.0 191.0 \n128 123.0 203.0 \n129 142.0 265.0 \n130 127.0 328.0 \n131 101.0 207.0 \n132 112.0 188.0 \n133 117.0 166.0 \n134 113.0 146.0 \n135 110.0 110.0 \n136 132.0 156.0 \n137 169.0 216.0 \n138 126.0 147.0 \n139 126.0 165.0 \n140 134.0 370.0 \n141 110.0 110.0 \n142 137.0 154.0 \n143 153.0 203.0 \n144 126.0 329.0 \n145 117.0 168.0 \n146 124.0 186.0 \n147 100.0 114.0 \n148 112.0 137.0 \n149 120.0 143.0 \n150 133.0 180.0 \n151 93.0 110.0 \n152 130.0 200.0 \n153 120.0 222.0 \n154 125.0 287.0 \n155 143.0 168.0 \n156 125.0 163.0 \n157 110.0 110.0 \n158 124.0 160.0 \n159 110.0 110.0 \n160 122.0 165.0 \n161 135.0 237.0 \n162 119.0 163.0 \n163 109.0 178.0 \n164 130.0 354.0 \n165 162.0 250.0 \n166 136.0 155.0 \n167 144.0 161.0 \n168 128.0 238.0 \n169 133.0 165.0 \n170 102.0 158.0 \n171 110.0 110.0 \n172 110.0 110.0 \n173 143.0 153.0 \n174 137.0 244.0 \n175 135.0 161.0 \n176 113.0 245.0 \n177 110.0 110.0 \n178 137.0 200.0 \n179 126.0 162.0 \n180 152.0 223.0 \n181 110.0 110.0 \n182 110.0 114.0 \n183 112.0 334.0 \n184 133.0 253.0 \n185 106.0 392.0 \n186 114.0 169.0 \n187 152.0 203.0 \n188 110.0 196.0 \n189 122.0 252.0 \n190 137.0 193.0 \n191 128.0 180.0 \n192 116.0 208.0 \n193 129.0 346.0 \n194 117.0 210.0 \n195 110.0 146.0 \n196 142.0 160.0 \n197 113.0 178.0 \n198 118.0 337.0 \n199 92.0 150.0 \n\n kl rewards / reward_fn / mean rewards / reward_fn / std \n0 0.045739 0.438560 0.000000 \n1 0.201123 0.239283 0.140017 \n2 0.033750 0.828506 0.152164 \n3 0.066646 0.676342 0.152164 \n4 0.044728 0.463845 0.050569 \n5 0.063451 0.676342 0.152164 \n6 0.059037 0.455604 0.124439 \n7 0.066614 0.413842 0.052068 \n8 0.057501 0.401810 0.010500 \n9 0.273347 0.222367 0.133778 \n10 0.136500 0.438152 0.081715 \n11 0.245289 0.107542 0.152164 \n12 0.088068 0.418726 0.059235 \n13 0.169696 0.283717 0.037757 \n14 0.102109 0.297410 0.179770 \n15 0.075884 0.275508 0.177936 \n16 0.065398 0.295342 0.179101 \n17 0.090292 0.752424 0.175704 \n18 0.078487 0.531888 0.066794 \n19 0.131891 0.389850 0.075231 \n20 0.048647 0.422520 0.078735 \n21 0.118428 0.291151 0.032208 \n22 0.096519 0.491764 0.049100 \n23 0.048325 0.351954 0.215680 \n24 0.047735 0.436135 0.110243 \n25 0.054242 0.501840 0.187630 \n26 0.096862 0.441485 0.001950 \n27 0.105513 0.330710 0.097117 \n28 0.477662 0.425175 0.028599 \n29 0.229918 0.676342 0.152164 \n30 0.082673 0.752424 0.175704 \n31 0.163536 0.442091 0.026940 \n32 0.089420 0.407767 0.194260 \n33 0.059572 0.449176 0.153489 \n34 0.084491 0.438566 0.015554 \n35 0.079262 0.330667 0.250889 \n36 0.136121 0.434510 0.018187 \n37 0.075638 0.209379 0.145371 \n38 0.070005 0.492002 0.147413 \n39 0.043416 0.266145 0.086264 \n40 0.096888 0.399579 0.103793 \n41 0.194719 0.574010 0.052500 \n42 0.095632 0.473594 0.046669 \n43 0.067037 0.366685 0.115896 \n44 0.074690 0.461601 0.051770 \n45 0.072218 0.326551 0.212770 \n46 0.058950 0.411985 0.107057 \n47 0.429925 0.421351 0.072040 \n48 0.093054 0.449966 0.021998 \n49 0.092123 0.456569 0.043482 \n50 0.071115 0.436710 0.044267 \n51 0.073914 0.696929 0.053889 \n52 0.077088 0.491485 0.003734 \n53 0.096172 0.412069 0.224425 \n54 0.069246 0.524390 0.299033 \n55 0.075970 0.548220 0.114612 \n56 0.074749 0.462801 0.090781 \n57 0.048557 0.650260 0.000000 \n58 0.127465 0.348786 0.123235 \n59 0.034682 0.285985 0.236171 \n60 0.090356 0.302995 0.178574 \n61 0.104580 0.468190 0.110350 \n62 0.093538 0.472180 0.282885 \n63 0.101624 0.539054 0.046605 \n64 0.098788 0.525639 0.095086 \n65 0.163230 0.409845 0.077256 \n66 0.070773 0.559505 0.081584 \n67 0.039133 0.673594 0.046669 \n68 0.082510 0.482635 0.014566 \n69 0.113112 0.438290 0.095949 \n70 0.100645 0.373385 0.121285 \n71 0.095642 0.328885 0.105130 \n72 0.088711 0.370761 0.101115 \n73 0.022040 0.696929 0.053889 \n74 0.091490 0.464020 0.092949 \n75 0.031428 0.696929 0.053889 \n76 0.038701 0.450160 0.079421 \n77 0.048427 0.464935 0.015750 \n78 0.098939 0.511635 0.038852 \n79 0.144878 0.476345 0.064423 \n80 0.072249 0.379810 0.199454 \n81 0.103491 0.442514 0.052545 \n82 0.077176 0.387320 0.116936 \n83 0.049649 0.483319 0.045058 \n84 0.058193 0.483020 0.049832 \n85 0.152530 0.430705 0.139750 \n86 0.092973 0.484510 0.018187 \n87 0.028071 0.650260 0.000000 \n88 0.181507 0.673594 0.046669 \n89 0.041483 0.371035 0.193620 \n90 0.043975 0.464935 0.015750 \n91 0.050338 0.316735 0.182232 \n92 0.049224 0.375995 0.204340 \n93 0.028467 0.673594 0.046669 \n94 0.049785 0.304260 0.257972 \n95 0.070081 0.376135 0.142429 \n96 0.072008 0.372245 0.210491 \n97 0.028605 0.673594 0.046669 \n98 0.028445 0.743598 0.000000 \n99 0.083007 0.501704 0.118444 \n100 0.064699 0.488270 0.044000 \n101 0.028692 0.673594 0.046669 \n102 0.049370 0.454060 0.076866 \n103 0.028820 0.436060 0.029698 \n104 0.046700 0.481369 0.046056 \n105 0.115197 0.451415 0.109445 \n106 0.064997 0.581864 0.090225 \n107 0.043585 0.464935 0.015750 \n108 0.058339 0.474469 0.103689 \n109 0.105060 0.413330 0.248198 \n110 0.424645 0.496455 0.218250 \n111 0.080222 0.495780 0.085686 \n112 0.089387 0.668440 0.102274 \n113 0.028732 0.751945 0.008630 \n114 0.028765 0.739000 0.000000 \n115 0.109365 0.665815 0.115322 \n116 0.087399 0.437015 0.138904 \n117 0.051438 0.336590 0.198202 \n118 0.092970 0.634130 0.161586 \n119 0.127245 0.428880 0.010216 \n120 0.183339 0.625250 0.115459 \n121 0.041193 0.403345 0.056279 \n122 0.105398 0.637730 0.064840 \n123 0.106493 0.464500 0.123319 \n124 0.082268 0.543215 0.082879 \n125 0.072173 0.692695 0.115909 \n126 0.110250 0.673690 0.107866 \n127 0.116218 0.517715 0.060186 \n128 0.135140 0.521995 0.068845 \n129 0.049414 0.588850 0.066772 \n130 0.057787 0.490190 0.101280 \n131 0.109531 0.644815 0.128879 \n132 0.092362 0.681565 0.115968 \n133 0.063423 0.679015 0.129510 \n134 0.057448 0.464955 0.076862 \n135 0.028768 0.743315 0.008630 \n136 0.093629 0.517345 0.103109 \n137 0.071225 0.603030 0.036726 \n138 0.084023 0.735175 0.108050 \n139 0.084536 0.658575 0.100384 \n140 0.058832 0.434540 0.188699 \n141 0.028767 0.739000 0.000000 \n142 0.074773 0.414665 0.024217 \n143 0.073668 0.579250 0.002252 \n144 0.104560 0.411105 0.012889 \n145 0.138347 0.422540 0.020326 \n146 0.184022 0.635670 0.110090 \n147 0.080490 0.621690 0.096735 \n148 0.062731 0.435740 0.186803 \n149 0.166411 0.439225 0.066061 \n150 0.054900 0.588220 0.016849 \n151 0.063102 0.747630 0.009965 \n152 0.059129 0.544645 0.074975 \n153 0.147442 0.389350 0.054560 \n154 0.115322 0.404125 0.024712 \n155 0.046176 0.303815 0.102252 \n156 0.103791 0.415420 0.020326 \n157 0.028765 0.743315 0.008630 \n158 0.045593 0.437125 0.193180 \n159 0.028763 0.743315 0.008630 \n160 0.103245 0.577640 0.007800 \n161 0.068059 0.551090 0.008189 \n162 0.086181 0.629565 0.113965 \n163 0.095757 0.626005 0.095163 \n164 0.065678 0.685125 0.111738 \n165 0.086924 0.412715 0.021271 \n166 0.101645 0.499380 0.208609 \n167 0.064099 0.545800 0.000000 \n168 0.053157 0.446650 0.184566 \n169 0.151453 0.410350 0.018187 \n170 0.078220 0.681565 0.097176 \n171 0.028755 0.743315 0.008630 \n172 0.028762 0.751945 0.008630 \n173 0.030943 0.565865 0.024217 \n174 0.057704 0.542240 0.004951 \n175 0.064726 0.417445 0.015859 \n176 0.042634 0.534365 0.034417 \n177 0.028742 0.743315 0.008630 \n178 0.048257 0.569425 0.015750 \n179 0.086826 0.561405 0.086123 \n180 0.081543 0.586490 0.007253 \n181 0.028752 0.739000 0.000000 \n182 0.029402 0.743315 0.008630 \n183 0.039695 0.343980 0.210234 \n184 0.115925 0.420130 0.212945 \n185 0.082160 0.681565 0.097176 \n186 0.056183 0.493375 0.082201 \n187 0.080742 0.481790 0.196229 \n188 0.088303 0.500605 0.101832 \n189 0.061111 0.513180 0.100658 \n190 0.279232 0.626080 0.115140 \n191 0.082597 0.504240 0.241419 \n192 0.079804 0.679015 0.104637 \n193 0.181960 0.409415 0.031267 \n194 0.060617 0.731575 0.089468 \n195 0.076874 0.505975 0.085279 \n196 0.090732 0.574080 0.020738 \n197 0.088252 0.665815 0.115322 \n198 0.077750 0.625250 0.094672 \n199 0.104137 0.398875 0.033887 ","text/html":"
\n\n
\n \n \n Step \n Training Loss \n reward \n reward_std \n completions / mean_length \n completions / min_length \n completions / max_length \n completions / clipped_ratio \n completions / mean_terminated_length \n completions / min_terminated_length \n completions / max_terminated_length \n kl \n rewards / reward_fn / mean \n rewards / reward_fn / std \n \n \n \n \n 0 \n 1 \n 0.0000 \n 0.438560 \n 0.000000 \n 181.75 \n 143.0 \n 256.0 \n 0.00 \n 181.750000 \n 143.0 \n 256.0 \n 0.045739 \n 0.438560 \n 0.000000 \n \n \n 1 \n 2 \n 0.0002 \n 0.239283 \n 0.140017 \n 148.00 \n 119.0 \n 184.0 \n 0.00 \n 148.000000 \n 119.0 \n 184.0 \n 0.201123 \n 0.239283 \n 0.140017 \n \n \n 2 \n 3 \n 0.0000 \n 0.828506 \n 0.152164 \n 169.75 \n 110.0 \n 349.0 \n 0.00 \n 169.750000 \n 110.0 \n 349.0 \n 0.033750 \n 0.828506 \n 0.152164 \n \n \n 3 \n 4 \n 0.0001 \n 0.676342 \n 0.152164 \n 106.00 \n 94.0 \n 110.0 \n 0.00 \n 106.000000 \n 94.0 \n 110.0 \n 0.066646 \n 0.676342 \n 0.152164 \n \n \n 4 \n 5 \n 0.0000 \n 0.463845 \n 0.050569 \n 162.00 \n 146.0 \n 188.0 \n 0.00 \n 162.000000 \n 146.0 \n 188.0 \n 0.044728 \n 0.463845 \n 0.050569 \n \n \n 5 \n 6 \n 0.0001 \n 0.676342 \n 0.152164 \n 123.75 \n 110.0 \n 139.0 \n 0.00 \n 123.750000 \n 110.0 \n 139.0 \n 0.063451 \n 0.676342 \n 0.152164 \n \n \n 6 \n 7 \n 0.0001 \n 0.455604 \n 0.124439 \n 136.50 \n 119.0 \n 152.0 \n 0.00 \n 136.500000 \n 119.0 \n 152.0 \n 0.059037 \n 0.455604 \n 0.124439 \n \n \n 7 \n 8 \n 0.0001 \n 0.413842 \n 0.052068 \n 164.50 \n 125.0 \n 203.0 \n 0.00 \n 164.500000 \n 125.0 \n 203.0 \n 0.066614 \n 0.413842 \n 0.052068 \n \n \n 8 \n 9 \n 0.0001 \n 0.401810 \n 0.010500 \n 142.50 \n 135.0 \n 147.0 \n 0.00 \n 142.500000 \n 135.0 \n 147.0 \n 0.057501 \n 0.401810 \n 0.010500 \n \n \n 9 \n 10 \n 0.0003 \n 0.222367 \n 0.133778 \n 150.50 \n 134.0 \n 169.0 \n 0.00 \n 150.500000 \n 134.0 \n 169.0 \n 0.273347 \n 0.222367 \n 0.133778 \n \n \n 10 \n 11 \n 0.0001 \n 0.438152 \n 0.081715 \n 142.75 \n 116.0 \n 168.0 \n 0.00 \n 142.750000 \n 116.0 \n 168.0 \n 0.136500 \n 0.438152 \n 0.081715 \n \n \n 11 \n 12 \n 0.0002 \n 0.107542 \n 0.152164 \n 199.50 \n 117.0 \n 400.0 \n 0.25 \n 132.666672 \n 117.0 \n 148.0 \n 0.245289 \n 0.107542 \n 0.152164 \n \n \n 12 \n 13 \n 0.0001 \n 0.418726 \n 0.059235 \n 254.00 \n 109.0 \n 400.0 \n 0.25 \n 205.333344 \n 109.0 \n 360.0 \n 0.088068 \n 0.418726 \n 0.059235 \n \n \n 13 \n 14 \n 0.0002 \n 0.283717 \n 0.037757 \n 131.75 \n 118.0 \n 141.0 \n 0.00 \n 131.750000 \n 118.0 \n 141.0 \n 0.169696 \n 0.283717 \n 0.037757 \n \n \n 14 \n 15 \n 0.0001 \n 0.297410 \n 0.179770 \n 162.00 \n 110.0 \n 205.0 \n 0.00 \n 162.000000 \n 110.0 \n 205.0 \n 0.102109 \n 0.297410 \n 0.179770 \n \n \n 15 \n 16 \n 0.0001 \n 0.275508 \n 0.177936 \n 226.00 \n 158.0 \n 400.0 \n 0.25 \n 168.000000 \n 158.0 \n 181.0 \n 0.075884 \n 0.275508 \n 0.177936 \n \n \n 16 \n 17 \n 0.0001 \n 0.295342 \n 0.179101 \n 173.75 \n 142.0 \n 206.0 \n 0.00 \n 173.750000 \n 142.0 \n 206.0 \n 0.065398 \n 0.295342 \n 0.179101 \n \n \n 17 \n 18 \n 0.0001 \n 0.752424 \n 0.175704 \n 142.25 \n 93.0 \n 190.0 \n 0.00 \n 142.250000 \n 93.0 \n 190.0 \n 0.090292 \n 0.752424 \n 0.175704 \n \n \n 18 \n 19 \n 0.0001 \n 0.531888 \n 0.066794 \n 179.00 \n 150.0 \n 207.0 \n 0.00 \n 179.000000 \n 150.0 \n 207.0 \n 0.078487 \n 0.531888 \n 0.066794 \n \n \n 19 \n 20 \n 0.0001 \n 0.389850 \n 0.075231 \n 202.25 \n 149.0 \n 299.0 \n 0.00 \n 202.250000 \n 149.0 \n 299.0 \n 0.131891 \n 0.389850 \n 0.075231 \n \n \n 20 \n 21 \n 0.0000 \n 0.422520 \n 0.078735 \n 145.00 \n 133.0 \n 157.0 \n 0.00 \n 145.000000 \n 133.0 \n 157.0 \n 0.048647 \n 0.422520 \n 0.078735 \n \n \n 21 \n 22 \n 0.0001 \n 0.291151 \n 0.032208 \n 149.00 \n 126.0 \n 165.0 \n 0.00 \n 149.000000 \n 126.0 \n 165.0 \n 0.118428 \n 0.291151 \n 0.032208 \n \n \n 22 \n 23 \n 0.0001 \n 0.491764 \n 0.049100 \n 150.00 \n 125.0 \n 178.0 \n 0.00 \n 150.000000 \n 125.0 \n 178.0 \n 0.096519 \n 0.491764 \n 0.049100 \n \n \n 23 \n 24 \n 0.0000 \n 0.351954 \n 0.215680 \n 179.75 \n 140.0 \n 250.0 \n 0.00 \n 179.750000 \n 140.0 \n 250.0 \n 0.048325 \n 0.351954 \n 0.215680 \n \n \n 24 \n 25 \n 0.0000 \n 0.436135 \n 0.110243 \n 146.75 \n 132.0 \n 172.0 \n 0.00 \n 146.750000 \n 132.0 \n 172.0 \n 0.047735 \n 0.436135 \n 0.110243 \n \n \n 25 \n 26 \n 0.0001 \n 0.501840 \n 0.187630 \n 163.50 \n 132.0 \n 227.0 \n 0.00 \n 163.500000 \n 132.0 \n 227.0 \n 0.054242 \n 0.501840 \n 0.187630 \n \n \n 26 \n 27 \n 0.0001 \n 0.441485 \n 0.001950 \n 133.50 \n 117.0 \n 158.0 \n 0.00 \n 133.500000 \n 117.0 \n 158.0 \n 0.096862 \n 0.441485 \n 0.001950 \n \n \n 27 \n 28 \n 0.0001 \n 0.330710 \n 0.097117 \n 137.00 \n 126.0 \n 151.0 \n 0.00 \n 137.000000 \n 126.0 \n 151.0 \n 0.105513 \n 0.330710 \n 0.097117 \n \n \n 28 \n 29 \n 0.0005 \n 0.425175 \n 0.028599 \n 131.25 \n 105.0 \n 147.0 \n 0.00 \n 131.250000 \n 105.0 \n 147.0 \n 0.477662 \n 0.425175 \n 0.028599 \n \n \n 29 \n 30 \n 0.0002 \n 0.676342 \n 0.152164 \n 186.00 \n 114.0 \n 311.0 \n 0.00 \n 186.000000 \n 114.0 \n 311.0 \n 0.229918 \n 0.676342 \n 0.152164 \n \n \n 30 \n 31 \n 0.0001 \n 0.752424 \n 0.175704 \n 192.50 \n 93.0 \n 362.0 \n 0.00 \n 192.500000 \n 93.0 \n 362.0 \n 0.082673 \n 0.752424 \n 0.175704 \n \n \n 31 \n 32 \n 0.0002 \n 0.442091 \n 0.026940 \n 115.00 \n 81.0 \n 138.0 \n 0.00 \n 115.000000 \n 81.0 \n 138.0 \n 0.163536 \n 0.442091 \n 0.026940 \n \n \n 32 \n 33 \n 0.0001 \n 0.407767 \n 0.194260 \n 164.25 \n 128.0 \n 233.0 \n 0.00 \n 164.250000 \n 128.0 \n 233.0 \n 0.089420 \n 0.407767 \n 0.194260 \n \n \n 33 \n 34 \n 0.0001 \n 0.449176 \n 0.153489 \n 194.25 \n 142.0 \n 277.0 \n 0.00 \n 194.250000 \n 142.0 \n 277.0 \n 0.059572 \n 0.449176 \n 0.153489 \n \n \n 34 \n 35 \n 0.0001 \n 0.438566 \n 0.015554 \n 152.50 \n 128.0 \n 168.0 \n 0.00 \n 152.500000 \n 128.0 \n 168.0 \n 0.084491 \n 0.438566 \n 0.015554 \n \n \n 35 \n 36 \n 0.0001 \n 0.330667 \n 0.250889 \n 149.25 \n 134.0 \n 179.0 \n 0.00 \n 149.250000 \n 134.0 \n 179.0 \n 0.079262 \n 0.330667 \n 0.250889 \n \n \n 36 \n 37 \n 0.0001 \n 0.434510 \n 0.018187 \n 137.00 \n 116.0 \n 169.0 \n 0.00 \n 137.000000 \n 116.0 \n 169.0 \n 0.136121 \n 0.434510 \n 0.018187 \n \n \n 37 \n 38 \n 0.0001 \n 0.209379 \n 0.145371 \n 136.75 \n 123.0 \n 150.0 \n 0.00 \n 136.750000 \n 123.0 \n 150.0 \n 0.075638 \n 0.209379 \n 0.145371 \n \n \n 38 \n 39 \n 0.0001 \n 0.492002 \n 0.147413 \n 136.25 \n 122.0 \n 157.0 \n 0.00 \n 136.250000 \n 122.0 \n 157.0 \n 0.070005 \n 0.492002 \n 0.147413 \n \n \n 39 \n 40 \n 0.0000 \n 0.266145 \n 0.086264 \n 149.00 \n 140.0 \n 154.0 \n 0.00 \n 149.000000 \n 140.0 \n 154.0 \n 0.043416 \n 0.266145 \n 0.086264 \n \n \n 40 \n 41 \n 0.0001 \n 0.399579 \n 0.103793 \n 148.00 \n 132.0 \n 171.0 \n 0.00 \n 148.000000 \n 132.0 \n 171.0 \n 0.096888 \n 0.399579 \n 0.103793 \n \n \n 41 \n 42 \n 0.0002 \n 0.574010 \n 0.052500 \n 116.25 \n 88.0 \n 191.0 \n 0.00 \n 116.250000 \n 88.0 \n 191.0 \n 0.194719 \n 0.574010 \n 0.052500 \n \n \n 42 \n 43 \n 0.0001 \n 0.473594 \n 0.046669 \n 140.75 \n 109.0 \n 175.0 \n 0.00 \n 140.750000 \n 109.0 \n 175.0 \n 0.095632 \n 0.473594 \n 0.046669 \n \n \n 43 \n 44 \n 0.0001 \n 0.366685 \n 0.115896 \n 136.75 \n 134.0 \n 143.0 \n 0.00 \n 136.750000 \n 134.0 \n 143.0 \n 0.067037 \n 0.366685 \n 0.115896 \n \n \n 44 \n 45 \n 0.0001 \n 0.461601 \n 0.051770 \n 172.25 \n 134.0 \n 220.0 \n 0.00 \n 172.250000 \n 134.0 \n 220.0 \n 0.074690 \n 0.461601 \n 0.051770 \n \n \n 45 \n 46 \n 0.0001 \n 0.326551 \n 0.212770 \n 238.00 \n 147.0 \n 400.0 \n 0.25 \n 184.000000 \n 147.0 \n 257.0 \n 0.072218 \n 0.326551 \n 0.212770 \n \n \n 46 \n 47 \n 0.0001 \n 0.411985 \n 0.107057 \n 141.75 \n 132.0 \n 148.0 \n 0.00 \n 141.750000 \n 132.0 \n 148.0 \n 0.058950 \n 0.411985 \n 0.107057 \n \n \n 47 \n 48 \n 0.0004 \n 0.421351 \n 0.072040 \n 141.25 \n 21.0 \n 233.0 \n 0.00 \n 141.250000 \n 21.0 \n 233.0 \n 0.429925 \n 0.421351 \n 0.072040 \n \n \n 48 \n 49 \n 0.0001 \n 0.449966 \n 0.021998 \n 173.75 \n 103.0 \n 228.0 \n 0.00 \n 173.750000 \n 103.0 \n 228.0 \n 0.093054 \n 0.449966 \n 0.021998 \n \n \n 49 \n 50 \n 0.0001 \n 0.456569 \n 0.043482 \n 141.50 \n 123.0 \n 170.0 \n 0.00 \n 141.500000 \n 123.0 \n 170.0 \n 0.092123 \n 0.456569 \n 0.043482 \n \n \n 50 \n 51 \n 0.0001 \n 0.436710 \n 0.044267 \n 194.50 \n 126.0 \n 254.0 \n 0.00 \n 194.500000 \n 126.0 \n 254.0 \n 0.071115 \n 0.436710 \n 0.044267 \n \n \n 51 \n 52 \n 0.0001 \n 0.696929 \n 0.053889 \n 158.50 \n 93.0 \n 302.0 \n 0.00 \n 158.500000 \n 93.0 \n 302.0 \n 0.073914 \n 0.696929 \n 0.053889 \n \n \n 52 \n 53 \n 0.0001 \n 0.491485 \n 0.003734 \n 163.75 \n 136.0 \n 212.0 \n 0.00 \n 163.750000 \n 136.0 \n 212.0 \n 0.077088 \n 0.491485 \n 0.003734 \n \n \n 53 \n 54 \n 0.0001 \n 0.412069 \n 0.224425 \n 127.25 \n 97.0 \n 142.0 \n 0.00 \n 127.250000 \n 97.0 \n 142.0 \n 0.096172 \n 0.412069 \n 0.224425 \n \n \n 54 \n 55 \n 0.0001 \n 0.524390 \n 0.299033 \n 142.00 \n 120.0 \n 177.0 \n 0.00 \n 142.000000 \n 120.0 \n 177.0 \n 0.069246 \n 0.524390 \n 0.299033 \n \n \n 55 \n 56 \n 0.0001 \n 0.548220 \n 0.114612 \n 150.00 \n 134.0 \n 158.0 \n 0.00 \n 150.000000 \n 134.0 \n 158.0 \n 0.075970 \n 0.548220 \n 0.114612 \n \n \n 56 \n 57 \n 0.0001 \n 0.462801 \n 0.090781 \n 177.25 \n 132.0 \n 264.0 \n 0.00 \n 177.250000 \n 132.0 \n 264.0 \n 0.074749 \n 0.462801 \n 0.090781 \n \n \n 57 \n 58 \n 0.0000 \n 0.650260 \n 0.000000 \n 126.25 \n 110.0 \n 146.0 \n 0.00 \n 126.250000 \n 110.0 \n 146.0 \n 0.048557 \n 0.650260 \n 0.000000 \n \n \n 58 \n 59 \n 0.0001 \n 0.348786 \n 0.123235 \n 145.75 \n 102.0 \n 205.0 \n 0.00 \n 145.750000 \n 102.0 \n 205.0 \n 0.127465 \n 0.348786 \n 0.123235 \n \n \n 59 \n 60 \n 0.0000 \n 0.285985 \n 0.236171 \n 204.50 \n 134.0 \n 400.0 \n 0.25 \n 139.333344 \n 134.0 \n 146.0 \n 0.034682 \n 0.285985 \n 0.236171 \n \n \n 60 \n 61 \n 0.0001 \n 0.302995 \n 0.178574 \n 203.50 \n 126.0 \n 400.0 \n 0.25 \n 138.000000 \n 126.0 \n 153.0 \n 0.090356 \n 0.302995 \n 0.178574 \n \n \n 61 \n 62 \n 0.0001 \n 0.468190 \n 0.110350 \n 204.75 \n 132.0 \n 400.0 \n 0.25 \n 139.666672 \n 132.0 \n 153.0 \n 0.104580 \n 0.468190 \n 0.110350 \n \n \n 62 \n 63 \n 0.0001 \n 0.472180 \n 0.282885 \n 151.25 \n 134.0 \n 187.0 \n 0.00 \n 151.250000 \n 134.0 \n 187.0 \n 0.093538 \n 0.472180 \n 0.282885 \n \n \n 63 \n 64 \n 0.0001 \n 0.539054 \n 0.046605 \n 190.75 \n 105.0 \n 400.0 \n 0.25 \n 121.000000 \n 105.0 \n 130.0 \n 0.101624 \n 0.539054 \n 0.046605 \n \n \n 64 \n 65 \n 0.0001 \n 0.525639 \n 0.095086 \n 115.50 \n 101.0 \n 136.0 \n 0.00 \n 115.500000 \n 101.0 \n 136.0 \n 0.098788 \n 0.525639 \n 0.095086 \n \n \n 65 \n 66 \n 0.0002 \n 0.409845 \n 0.077256 \n 139.25 \n 135.0 \n 147.0 \n 0.00 \n 139.250000 \n 135.0 \n 147.0 \n 0.163230 \n 0.409845 \n 0.077256 \n \n \n 66 \n 67 \n 0.0001 \n 0.559505 \n 0.081584 \n 139.50 \n 111.0 \n 179.0 \n 0.00 \n 139.500000 \n 111.0 \n 179.0 \n 0.070773 \n 0.559505 \n 0.081584 \n \n \n 67 \n 68 \n 0.0000 \n 0.673594 \n 0.046669 \n 160.50 \n 110.0 \n 206.0 \n 0.00 \n 160.500000 \n 110.0 \n 206.0 \n 0.039133 \n 0.673594 \n 0.046669 \n \n \n 68 \n 69 \n 0.0001 \n 0.482635 \n 0.014566 \n 195.75 \n 140.0 \n 285.0 \n 0.00 \n 195.750000 \n 140.0 \n 285.0 \n 0.082510 \n 0.482635 \n 0.014566 \n \n \n 69 \n 70 \n 0.0001 \n 0.438290 \n 0.095949 \n 141.50 \n 82.0 \n 212.0 \n 0.00 \n 141.500000 \n 82.0 \n 212.0 \n 0.113112 \n 0.438290 \n 0.095949 \n \n \n 70 \n 71 \n 0.0001 \n 0.373385 \n 0.121285 \n 240.00 \n 160.0 \n 395.0 \n 0.00 \n 240.000000 \n 160.0 \n 395.0 \n 0.100645 \n 0.373385 \n 0.121285 \n \n \n 71 \n 72 \n 0.0001 \n 0.328885 \n 0.105130 \n 151.00 \n 124.0 \n 178.0 \n 0.00 \n 151.000000 \n 124.0 \n 178.0 \n 0.095642 \n 0.328885 \n 0.105130 \n \n \n 72 \n 73 \n 0.0001 \n 0.370761 \n 0.101115 \n 155.00 \n 128.0 \n 213.0 \n 0.00 \n 155.000000 \n 128.0 \n 213.0 \n 0.088711 \n 0.370761 \n 0.101115 \n \n \n 73 \n 74 \n 0.0000 \n 0.696929 \n 0.053889 \n 131.25 \n 110.0 \n 195.0 \n 0.00 \n 131.250000 \n 110.0 \n 195.0 \n 0.022040 \n 0.696929 \n 0.053889 \n \n \n 74 \n 75 \n 0.0001 \n 0.464020 \n 0.092949 \n 144.50 \n 124.0 \n 173.0 \n 0.00 \n 144.500000 \n 124.0 \n 173.0 \n 0.091490 \n 0.464020 \n 0.092949 \n \n \n 75 \n 76 \n 0.0000 \n 0.696929 \n 0.053889 \n 132.75 \n 110.0 \n 182.0 \n 0.00 \n 132.750000 \n 110.0 \n 182.0 \n 0.031428 \n 0.696929 \n 0.053889 \n \n \n 76 \n 77 \n 0.0000 \n 0.450160 \n 0.079421 \n 149.00 \n 140.0 \n 154.0 \n 0.00 \n 149.000000 \n 140.0 \n 154.0 \n 0.038701 \n 0.450160 \n 0.079421 \n \n \n 77 \n 78 \n 0.0000 \n 0.464935 \n 0.015750 \n 151.75 \n 134.0 \n 189.0 \n 0.00 \n 151.750000 \n 134.0 \n 189.0 \n 0.048427 \n 0.464935 \n 0.015750 \n \n \n 78 \n 79 \n 0.0001 \n 0.511635 \n 0.038852 \n 182.25 \n 121.0 \n 242.0 \n 0.00 \n 182.250000 \n 121.0 \n 242.0 \n 0.098939 \n 0.511635 \n 0.038852 \n \n \n 79 \n 80 \n 0.0001 \n 0.476345 \n 0.064423 \n 182.50 \n 107.0 \n 400.0 \n 0.25 \n 110.000000 \n 107.0 \n 113.0 \n 0.144878 \n 0.476345 \n 0.064423 \n \n \n 80 \n 81 \n 0.0001 \n 0.379810 \n 0.199454 \n 188.50 \n 103.0 \n 400.0 \n 0.25 \n 118.000000 \n 103.0 \n 144.0 \n 0.072249 \n 0.379810 \n 0.199454 \n \n \n 81 \n 82 \n 0.0001 \n 0.442514 \n 0.052545 \n 127.75 \n 107.0 \n 143.0 \n 0.00 \n 127.750000 \n 107.0 \n 143.0 \n 0.103491 \n 0.442514 \n 0.052545 \n \n \n 82 \n 83 \n 0.0001 \n 0.387320 \n 0.116936 \n 159.75 \n 127.0 \n 233.0 \n 0.00 \n 159.750000 \n 127.0 \n 233.0 \n 0.077176 \n 0.387320 \n 0.116936 \n \n \n 83 \n 84 \n 0.0000 \n 0.483319 \n 0.045058 \n 176.25 \n 170.0 \n 184.0 \n 0.00 \n 176.250000 \n 170.0 \n 184.0 \n 0.049649 \n 0.483319 \n 0.045058 \n \n \n 84 \n 85 \n 0.0001 \n 0.483020 \n 0.049832 \n 187.25 \n 137.0 \n 291.0 \n 0.00 \n 187.250000 \n 137.0 \n 291.0 \n 0.058193 \n 0.483020 \n 0.049832 \n \n \n 85 \n 86 \n 0.0002 \n 0.430705 \n 0.139750 \n 129.75 \n 111.0 \n 145.0 \n 0.00 \n 129.750000 \n 111.0 \n 145.0 \n 0.152530 \n 0.430705 \n 0.139750 \n \n \n 86 \n 87 \n 0.0001 \n 0.484510 \n 0.018187 \n 137.25 \n 103.0 \n 192.0 \n 0.00 \n 137.250000 \n 103.0 \n 192.0 \n 0.092973 \n 0.484510 \n 0.018187 \n \n \n 87 \n 88 \n 0.0000 \n 0.650260 \n 0.000000 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028071 \n 0.650260 \n 0.000000 \n \n \n 88 \n 89 \n 0.0002 \n 0.673594 \n 0.046669 \n 124.50 \n 110.0 \n 144.0 \n 0.00 \n 124.500000 \n 110.0 \n 144.0 \n 0.181507 \n 0.673594 \n 0.046669 \n \n \n 89 \n 90 \n 0.0000 \n 0.371035 \n 0.193620 \n 208.50 \n 141.0 \n 400.0 \n 0.25 \n 144.666672 \n 141.0 \n 149.0 \n 0.041483 \n 0.371035 \n 0.193620 \n \n \n 90 \n 91 \n 0.0000 \n 0.464935 \n 0.015750 \n 147.50 \n 141.0 \n 156.0 \n 0.00 \n 147.500000 \n 141.0 \n 156.0 \n 0.043975 \n 0.464935 \n 0.015750 \n \n \n 91 \n 92 \n 0.0001 \n 0.316735 \n 0.182232 \n 131.25 \n 124.0 \n 137.0 \n 0.00 \n 131.250000 \n 124.0 \n 137.0 \n 0.050338 \n 0.316735 \n 0.182232 \n \n \n 92 \n 93 \n 0.0000 \n 0.375995 \n 0.204340 \n 153.25 \n 136.0 \n 165.0 \n 0.00 \n 153.250000 \n 136.0 \n 165.0 \n 0.049224 \n 0.375995 \n 0.204340 \n \n \n 93 \n 94 \n 0.0000 \n 0.673594 \n 0.046669 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028467 \n 0.673594 \n 0.046669 \n \n \n 94 \n 95 \n 0.0000 \n 0.304260 \n 0.257972 \n 228.00 \n 119.0 \n 400.0 \n 0.25 \n 170.666672 \n 119.0 \n 236.0 \n 0.049785 \n 0.304260 \n 0.257972 \n \n \n 95 \n 96 \n 0.0001 \n 0.376135 \n 0.142429 \n 141.75 \n 132.0 \n 151.0 \n 0.00 \n 141.750000 \n 132.0 \n 151.0 \n 0.070081 \n 0.376135 \n 0.142429 \n \n \n 96 \n 97 \n 0.0001 \n 0.372245 \n 0.210491 \n 137.00 \n 110.0 \n 160.0 \n 0.00 \n 137.000000 \n 110.0 \n 160.0 \n 0.072008 \n 0.372245 \n 0.210491 \n \n \n 97 \n 98 \n 0.0000 \n 0.673594 \n 0.046669 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028605 \n 0.673594 \n 0.046669 \n \n \n 98 \n 99 \n 0.0000 \n 0.743598 \n 0.000000 \n 111.00 \n 110.0 \n 114.0 \n 0.00 \n 111.000000 \n 110.0 \n 114.0 \n 0.028445 \n 0.743598 \n 0.000000 \n \n \n 99 \n 100 \n 0.0001 \n 0.501704 \n 0.118444 \n 137.50 \n 125.0 \n 145.0 \n 0.00 \n 137.500000 \n 125.0 \n 145.0 \n 0.083007 \n 0.501704 \n 0.118444 \n \n \n 100 \n 101 \n 0.0001 \n 0.488270 \n 0.044000 \n 175.50 \n 143.0 \n 223.0 \n 0.00 \n 175.500000 \n 143.0 \n 223.0 \n 0.064699 \n 0.488270 \n 0.044000 \n \n \n 101 \n 102 \n 0.0000 \n 0.673594 \n 0.046669 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028692 \n 0.673594 \n 0.046669 \n \n \n 102 \n 103 \n 0.0000 \n 0.454060 \n 0.076866 \n 150.75 \n 142.0 \n 169.0 \n 0.00 \n 150.750000 \n 142.0 \n 169.0 \n 0.049370 \n 0.454060 \n 0.076866 \n \n \n 103 \n 104 \n 0.0000 \n 0.436060 \n 0.029699 \n 162.50 \n 126.0 \n 258.0 \n 0.00 \n 162.500000 \n 126.0 \n 258.0 \n 0.028820 \n 0.436060 \n 0.029698 \n \n \n 104 \n 105 \n 0.0000 \n 0.481369 \n 0.046056 \n 154.50 \n 142.0 \n 169.0 \n 0.00 \n 154.500000 \n 142.0 \n 169.0 \n 0.046700 \n 0.481369 \n 0.046056 \n \n \n 105 \n 106 \n 0.0001 \n 0.451415 \n 0.109445 \n 160.25 \n 126.0 \n 188.0 \n 0.00 \n 160.250000 \n 126.0 \n 188.0 \n 0.115197 \n 0.451415 \n 0.109445 \n \n \n 106 \n 107 \n 0.0001 \n 0.581864 \n 0.090225 \n 190.75 \n 139.0 \n 289.0 \n 0.00 \n 190.750000 \n 139.0 \n 289.0 \n 0.064997 \n 0.581864 \n 0.090225 \n \n \n 107 \n 108 \n 0.0000 \n 0.464935 \n 0.015750 \n 154.50 \n 142.0 \n 170.0 \n 0.00 \n 154.500000 \n 142.0 \n 170.0 \n 0.043585 \n 0.464935 \n 0.015750 \n \n \n 108 \n 109 \n 0.0001 \n 0.474469 \n 0.103689 \n 202.25 \n 144.0 \n 270.0 \n 0.00 \n 202.250000 \n 144.0 \n 270.0 \n 0.058339 \n 0.474469 \n 0.103689 \n \n \n 109 \n 110 \n 0.0001 \n 0.413330 \n 0.248198 \n 180.50 \n 127.0 \n 300.0 \n 0.00 \n 180.500000 \n 127.0 \n 300.0 \n 0.105060 \n 0.413330 \n 0.248198 \n \n \n 110 \n 111 \n 0.0004 \n 0.496455 \n 0.218250 \n 169.50 \n 129.0 \n 264.0 \n 0.00 \n 169.500000 \n 129.0 \n 264.0 \n 0.424645 \n 0.496455 \n 0.218250 \n \n \n 111 \n 112 \n 0.0001 \n 0.495780 \n 0.085686 \n 155.00 \n 118.0 \n 199.0 \n 0.00 \n 155.000000 \n 118.0 \n 199.0 \n 0.080222 \n 0.495780 \n 0.085686 \n \n \n 112 \n 113 \n 0.0001 \n 0.668440 \n 0.102274 \n 128.75 \n 98.0 \n 154.0 \n 0.00 \n 128.750000 \n 98.0 \n 154.0 \n 0.089387 \n 0.668440 \n 0.102274 \n \n \n 113 \n 114 \n 0.0000 \n 0.751945 \n 0.008630 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028732 \n 0.751945 \n 0.008630 \n \n \n 114 \n 115 \n 0.0000 \n 0.739000 \n 0.000000 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028765 \n 0.739000 \n 0.000000 \n \n \n 115 \n 116 \n 0.0001 \n 0.665815 \n 0.115322 \n 130.00 \n 103.0 \n 177.0 \n 0.00 \n 130.000000 \n 103.0 \n 177.0 \n 0.109365 \n 0.665815 \n 0.115322 \n \n \n 116 \n 117 \n 0.0001 \n 0.437015 \n 0.138904 \n 156.75 \n 125.0 \n 210.0 \n 0.00 \n 156.750000 \n 125.0 \n 210.0 \n 0.087399 \n 0.437015 \n 0.138904 \n \n \n 117 \n 118 \n 0.0001 \n 0.336590 \n 0.198202 \n 147.00 \n 116.0 \n 168.0 \n 0.00 \n 147.000000 \n 116.0 \n 168.0 \n 0.051438 \n 0.336590 \n 0.198202 \n \n \n 118 \n 119 \n 0.0001 \n 0.634130 \n 0.161586 \n 137.75 \n 116.0 \n 172.0 \n 0.00 \n 137.750000 \n 116.0 \n 172.0 \n 0.092970 \n 0.634130 \n 0.161586 \n \n \n 119 \n 120 \n 0.0001 \n 0.428880 \n 0.010216 \n 187.75 \n 144.0 \n 233.0 \n 0.00 \n 187.750000 \n 144.0 \n 233.0 \n 0.127245 \n 0.428880 \n 0.010216 \n \n \n 120 \n 121 \n 0.0002 \n 0.625250 \n 0.115459 \n 123.75 \n 82.0 \n 159.0 \n 0.00 \n 123.750000 \n 82.0 \n 159.0 \n 0.183339 \n 0.625250 \n 0.115459 \n \n \n 121 \n 122 \n 0.0000 \n 0.403345 \n 0.056279 \n 143.00 \n 137.0 \n 153.0 \n 0.00 \n 143.000000 \n 137.0 \n 153.0 \n 0.041193 \n 0.403345 \n 0.056279 \n \n \n 122 \n 123 \n 0.0001 \n 0.637730 \n 0.064840 \n 208.50 \n 113.0 \n 400.0 \n 0.25 \n 144.666672 \n 113.0 \n 182.0 \n 0.105398 \n 0.637730 \n 0.064840 \n \n \n 123 \n 124 \n 0.0001 \n 0.464500 \n 0.123319 \n 120.50 \n 65.0 \n 164.0 \n 0.00 \n 120.500000 \n 65.0 \n 164.0 \n 0.106493 \n 0.464500 \n 0.123319 \n \n \n 124 \n 125 \n 0.0001 \n 0.543215 \n 0.082879 \n 144.00 \n 130.0 \n 164.0 \n 0.00 \n 144.000000 \n 130.0 \n 164.0 \n 0.082268 \n 0.543215 \n 0.082879 \n \n \n 125 \n 126 \n 0.0001 \n 0.692695 \n 0.115909 \n 114.00 \n 110.0 \n 126.0 \n 0.00 \n 114.000000 \n 110.0 \n 126.0 \n 0.072173 \n 0.692695 \n 0.115909 \n \n \n 126 \n 127 \n 0.0001 \n 0.673690 \n 0.107866 \n 105.00 \n 98.0 \n 116.0 \n 0.00 \n 105.000000 \n 98.0 \n 116.0 \n 0.110250 \n 0.673690 \n 0.107866 \n \n \n 127 \n 128 \n 0.0001 \n 0.517715 \n 0.060186 \n 160.75 \n 135.0 \n 191.0 \n 0.00 \n 160.750000 \n 135.0 \n 191.0 \n 0.116218 \n 0.517715 \n 0.060186 \n \n \n 128 \n 129 \n 0.0001 \n 0.521995 \n 0.068845 \n 163.75 \n 123.0 \n 203.0 \n 0.00 \n 163.750000 \n 123.0 \n 203.0 \n 0.135140 \n 0.521995 \n 0.068845 \n \n \n 129 \n 130 \n 0.0000 \n 0.588850 \n 0.066772 \n 183.50 \n 142.0 \n 265.0 \n 0.00 \n 183.500000 \n 142.0 \n 265.0 \n 0.049414 \n 0.588850 \n 0.066772 \n \n \n 130 \n 131 \n 0.0001 \n 0.490190 \n 0.101280 \n 199.25 \n 127.0 \n 328.0 \n 0.00 \n 199.250000 \n 127.0 \n 328.0 \n 0.057787 \n 0.490190 \n 0.101280 \n \n \n 131 \n 132 \n 0.0001 \n 0.644815 \n 0.128879 \n 164.00 \n 101.0 \n 207.0 \n 0.00 \n 164.000000 \n 101.0 \n 207.0 \n 0.109531 \n 0.644815 \n 0.128879 \n \n \n 132 \n 133 \n 0.0001 \n 0.681565 \n 0.115968 \n 165.00 \n 112.0 \n 188.0 \n 0.00 \n 165.000000 \n 112.0 \n 188.0 \n 0.092362 \n 0.681565 \n 0.115968 \n \n \n 133 \n 134 \n 0.0001 \n 0.679015 \n 0.129510 \n 140.25 \n 117.0 \n 166.0 \n 0.00 \n 140.250000 \n 117.0 \n 166.0 \n 0.063423 \n 0.679015 \n 0.129510 \n \n \n 134 \n 135 \n 0.0001 \n 0.464955 \n 0.076862 \n 128.00 \n 113.0 \n 146.0 \n 0.00 \n 128.000000 \n 113.0 \n 146.0 \n 0.057448 \n 0.464955 \n 0.076862 \n \n \n 135 \n 136 \n 0.0000 \n 0.743315 \n 0.008630 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028768 \n 0.743315 \n 0.008630 \n \n \n 136 \n 137 \n 0.0001 \n 0.517345 \n 0.103109 \n 144.25 \n 132.0 \n 156.0 \n 0.00 \n 144.250000 \n 132.0 \n 156.0 \n 0.093629 \n 0.517345 \n 0.103109 \n \n \n 137 \n 138 \n 0.0001 \n 0.603030 \n 0.036726 \n 244.75 \n 169.0 \n 400.0 \n 0.25 \n 193.000000 \n 169.0 \n 216.0 \n 0.071225 \n 0.603030 \n 0.036726 \n \n \n 138 \n 139 \n 0.0001 \n 0.735175 \n 0.108050 \n 140.00 \n 126.0 \n 147.0 \n 0.00 \n 140.000000 \n 126.0 \n 147.0 \n 0.084023 \n 0.735175 \n 0.108050 \n \n \n 139 \n 140 \n 0.0001 \n 0.658575 \n 0.100384 \n 139.75 \n 126.0 \n 165.0 \n 0.00 \n 139.750000 \n 126.0 \n 165.0 \n 0.084536 \n 0.658575 \n 0.100384 \n \n \n 140 \n 141 \n 0.0001 \n 0.434540 \n 0.188699 \n 200.00 \n 134.0 \n 370.0 \n 0.00 \n 200.000000 \n 134.0 \n 370.0 \n 0.058832 \n 0.434540 \n 0.188699 \n \n \n 141 \n 142 \n 0.0000 \n 0.739000 \n 0.000000 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028767 \n 0.739000 \n 0.000000 \n \n \n 142 \n 143 \n 0.0001 \n 0.414665 \n 0.024217 \n 143.25 \n 137.0 \n 154.0 \n 0.00 \n 143.250000 \n 137.0 \n 154.0 \n 0.074773 \n 0.414665 \n 0.024217 \n \n \n 143 \n 144 \n 0.0001 \n 0.579250 \n 0.002252 \n 171.75 \n 153.0 \n 203.0 \n 0.00 \n 171.750000 \n 153.0 \n 203.0 \n 0.073668 \n 0.579250 \n 0.002252 \n \n \n 144 \n 145 \n 0.0001 \n 0.411105 \n 0.012889 \n 181.50 \n 126.0 \n 329.0 \n 0.00 \n 181.500000 \n 126.0 \n 329.0 \n 0.104560 \n 0.411105 \n 0.012889 \n \n \n 145 \n 146 \n 0.0001 \n 0.422540 \n 0.020326 \n 138.00 \n 117.0 \n 168.0 \n 0.00 \n 138.000000 \n 117.0 \n 168.0 \n 0.138347 \n 0.422540 \n 0.020326 \n \n \n 146 \n 147 \n 0.0002 \n 0.635670 \n 0.110090 \n 153.50 \n 124.0 \n 186.0 \n 0.00 \n 153.500000 \n 124.0 \n 186.0 \n 0.184022 \n 0.635670 \n 0.110090 \n \n \n 147 \n 148 \n 0.0001 \n 0.621690 \n 0.096735 \n 253.50 \n 100.0 \n 400.0 \n 0.50 \n 107.000000 \n 100.0 \n 114.0 \n 0.080490 \n 0.621690 \n 0.096735 \n \n \n 148 \n 149 \n 0.0001 \n 0.435740 \n 0.186803 \n 191.00 \n 112.0 \n 400.0 \n 0.25 \n 121.333336 \n 112.0 \n 137.0 \n 0.062731 \n 0.435740 \n 0.186803 \n \n \n 149 \n 150 \n 0.0002 \n 0.439225 \n 0.066061 \n 129.00 \n 120.0 \n 143.0 \n 0.00 \n 129.000000 \n 120.0 \n 143.0 \n 0.166411 \n 0.439225 \n 0.066061 \n \n \n 150 \n 151 \n 0.0001 \n 0.588220 \n 0.016849 \n 154.75 \n 133.0 \n 180.0 \n 0.00 \n 154.750000 \n 133.0 \n 180.0 \n 0.054900 \n 0.588220 \n 0.016849 \n \n \n 151 \n 152 \n 0.0001 \n 0.747630 \n 0.009965 \n 105.75 \n 93.0 \n 110.0 \n 0.00 \n 105.750000 \n 93.0 \n 110.0 \n 0.063102 \n 0.747630 \n 0.009965 \n \n \n 152 \n 153 \n 0.0001 \n 0.544645 \n 0.074975 \n 170.75 \n 130.0 \n 200.0 \n 0.00 \n 170.750000 \n 130.0 \n 200.0 \n 0.059129 \n 0.544645 \n 0.074975 \n \n \n 153 \n 154 \n 0.0001 \n 0.389350 \n 0.054560 \n 158.75 \n 120.0 \n 222.0 \n 0.00 \n 158.750000 \n 120.0 \n 222.0 \n 0.147442 \n 0.389350 \n 0.054560 \n \n \n 154 \n 155 \n 0.0001 \n 0.404125 \n 0.024712 \n 171.00 \n 125.0 \n 287.0 \n 0.00 \n 171.000000 \n 125.0 \n 287.0 \n 0.115322 \n 0.404125 \n 0.024712 \n \n \n 155 \n 156 \n 0.0000 \n 0.303815 \n 0.102252 \n 216.25 \n 143.0 \n 400.0 \n 0.25 \n 155.000000 \n 143.0 \n 168.0 \n 0.046176 \n 0.303815 \n 0.102252 \n \n \n 156 \n 157 \n 0.0001 \n 0.415420 \n 0.020326 \n 140.50 \n 125.0 \n 163.0 \n 0.00 \n 140.500000 \n 125.0 \n 163.0 \n 0.103791 \n 0.415420 \n 0.020326 \n \n \n 157 \n 158 \n 0.0000 \n 0.743315 \n 0.008630 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028765 \n 0.743315 \n 0.008630 \n \n \n 158 \n 159 \n 0.0000 \n 0.437125 \n 0.193180 \n 143.50 \n 124.0 \n 160.0 \n 0.00 \n 143.500000 \n 124.0 \n 160.0 \n 0.045593 \n 0.437125 \n 0.193180 \n \n \n 159 \n 160 \n 0.0000 \n 0.743315 \n 0.008630 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028763 \n 0.743315 \n 0.008630 \n \n \n 160 \n 161 \n 0.0001 \n 0.577640 \n 0.007800 \n 139.50 \n 122.0 \n 165.0 \n 0.00 \n 139.500000 \n 122.0 \n 165.0 \n 0.103245 \n 0.577640 \n 0.007800 \n \n \n 161 \n 162 \n 0.0001 \n 0.551090 \n 0.008189 \n 179.25 \n 135.0 \n 237.0 \n 0.00 \n 179.250000 \n 135.0 \n 237.0 \n 0.068059 \n 0.551090 \n 0.008189 \n \n \n 162 \n 163 \n 0.0001 \n 0.629565 \n 0.113965 \n 146.50 \n 119.0 \n 163.0 \n 0.00 \n 146.500000 \n 119.0 \n 163.0 \n 0.086181 \n 0.629565 \n 0.113965 \n \n \n 163 \n 164 \n 0.0001 \n 0.626005 \n 0.095162 \n 132.75 \n 109.0 \n 178.0 \n 0.00 \n 132.750000 \n 109.0 \n 178.0 \n 0.095757 \n 0.626005 \n 0.095163 \n \n \n 164 \n 165 \n 0.0001 \n 0.685125 \n 0.111738 \n 218.00 \n 130.0 \n 354.0 \n 0.00 \n 218.000000 \n 130.0 \n 354.0 \n 0.065678 \n 0.685125 \n 0.111738 \n \n \n 165 \n 166 \n 0.0001 \n 0.412715 \n 0.021271 \n 197.00 \n 162.0 \n 250.0 \n 0.00 \n 197.000000 \n 162.0 \n 250.0 \n 0.086924 \n 0.412715 \n 0.021271 \n \n \n 166 \n 167 \n 0.0001 \n 0.499380 \n 0.208609 \n 210.75 \n 136.0 \n 400.0 \n 0.25 \n 147.666672 \n 136.0 \n 155.0 \n 0.101645 \n 0.499380 \n 0.208609 \n \n \n 167 \n 168 \n 0.0001 \n 0.545800 \n 0.000000 \n 152.00 \n 144.0 \n 161.0 \n 0.00 \n 152.000000 \n 144.0 \n 161.0 \n 0.064099 \n 0.545800 \n 0.000000 \n \n \n 168 \n 169 \n 0.0001 \n 0.446650 \n 0.184566 \n 172.25 \n 128.0 \n 238.0 \n 0.00 \n 172.250000 \n 128.0 \n 238.0 \n 0.053157 \n 0.446650 \n 0.184566 \n \n \n 169 \n 170 \n 0.0002 \n 0.410350 \n 0.018187 \n 149.50 \n 133.0 \n 165.0 \n 0.00 \n 149.500000 \n 133.0 \n 165.0 \n 0.151453 \n 0.410350 \n 0.018187 \n \n \n 170 \n 171 \n 0.0001 \n 0.681565 \n 0.097176 \n 138.25 \n 102.0 \n 158.0 \n 0.00 \n 138.250000 \n 102.0 \n 158.0 \n 0.078220 \n 0.681565 \n 0.097176 \n \n \n 171 \n 172 \n 0.0000 \n 0.743315 \n 0.008630 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028755 \n 0.743315 \n 0.008630 \n \n \n 172 \n 173 \n 0.0000 \n 0.751945 \n 0.008630 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028762 \n 0.751945 \n 0.008630 \n \n \n 173 \n 174 \n 0.0000 \n 0.565865 \n 0.024217 \n 148.75 \n 143.0 \n 153.0 \n 0.00 \n 148.750000 \n 143.0 \n 153.0 \n 0.030943 \n 0.565865 \n 0.024217 \n \n \n 174 \n 175 \n 0.0001 \n 0.542240 \n 0.004951 \n 173.25 \n 137.0 \n 244.0 \n 0.00 \n 173.250000 \n 137.0 \n 244.0 \n 0.057704 \n 0.542240 \n 0.004951 \n \n \n 175 \n 176 \n 0.0001 \n 0.417445 \n 0.015859 \n 145.00 \n 135.0 \n 161.0 \n 0.00 \n 145.000000 \n 135.0 \n 161.0 \n 0.064726 \n 0.417445 \n 0.015859 \n \n \n 176 \n 177 \n 0.0000 \n 0.534365 \n 0.034417 \n 171.75 \n 113.0 \n 245.0 \n 0.00 \n 171.750000 \n 113.0 \n 245.0 \n 0.042634 \n 0.534365 \n 0.034417 \n \n \n 177 \n 178 \n 0.0000 \n 0.743315 \n 0.008630 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028742 \n 0.743315 \n 0.008630 \n \n \n 178 \n 179 \n 0.0000 \n 0.569425 \n 0.015750 \n 156.75 \n 137.0 \n 200.0 \n 0.00 \n 156.750000 \n 137.0 \n 200.0 \n 0.048257 \n 0.569425 \n 0.015750 \n \n \n 179 \n 180 \n 0.0001 \n 0.561405 \n 0.086123 \n 139.75 \n 126.0 \n 162.0 \n 0.00 \n 139.750000 \n 126.0 \n 162.0 \n 0.086826 \n 0.561405 \n 0.086123 \n \n \n 180 \n 181 \n 0.0001 \n 0.586490 \n 0.007253 \n 175.50 \n 152.0 \n 223.0 \n 0.00 \n 175.500000 \n 152.0 \n 223.0 \n 0.081543 \n 0.586490 \n 0.007253 \n \n \n 181 \n 182 \n 0.0000 \n 0.739000 \n 0.000000 \n 110.00 \n 110.0 \n 110.0 \n 0.00 \n 110.000000 \n 110.0 \n 110.0 \n 0.028752 \n 0.739000 \n 0.000000 \n \n \n 182 \n 183 \n 0.0000 \n 0.743315 \n 0.008630 \n 111.00 \n 110.0 \n 114.0 \n 0.00 \n 111.000000 \n 110.0 \n 114.0 \n 0.029402 \n 0.743315 \n 0.008630 \n \n \n 183 \n 184 \n 0.0000 \n 0.343980 \n 0.210234 \n 182.25 \n 112.0 \n 334.0 \n 0.00 \n 182.250000 \n 112.0 \n 334.0 \n 0.039695 \n 0.343980 \n 0.210234 \n \n \n 184 \n 185 \n 0.0001 \n 0.420130 \n 0.212945 \n 177.25 \n 133.0 \n 253.0 \n 0.00 \n 177.250000 \n 133.0 \n 253.0 \n 0.115925 \n 0.420130 \n 0.212945 \n \n \n 185 \n 186 \n 0.0001 \n 0.681565 \n 0.097176 \n 212.50 \n 106.0 \n 392.0 \n 0.00 \n 212.500000 \n 106.0 \n 392.0 \n 0.082160 \n 0.681565 \n 0.097176 \n \n \n 186 \n 187 \n 0.0001 \n 0.493375 \n 0.082201 \n 135.25 \n 114.0 \n 169.0 \n 0.00 \n 135.250000 \n 114.0 \n 169.0 \n 0.056183 \n 0.493375 \n 0.082201 \n \n \n 187 \n 188 \n 0.0001 \n 0.481790 \n 0.196229 \n 168.75 \n 152.0 \n 203.0 \n 0.00 \n 168.750000 \n 152.0 \n 203.0 \n 0.080742 \n 0.481790 \n 0.196229 \n \n \n 188 \n 189 \n 0.0001 \n 0.500605 \n 0.101832 \n 157.50 \n 110.0 \n 196.0 \n 0.00 \n 157.500000 \n 110.0 \n 196.0 \n 0.088303 \n 0.500605 \n 0.101832 \n \n \n 189 \n 190 \n 0.0001 \n 0.513180 \n 0.100658 \n 184.75 \n 122.0 \n 252.0 \n 0.00 \n 184.750000 \n 122.0 \n 252.0 \n 0.061111 \n 0.513180 \n 0.100658 \n \n \n 190 \n 191 \n 0.0003 \n 0.626080 \n 0.115140 \n 155.75 \n 137.0 \n 193.0 \n 0.00 \n 155.750000 \n 137.0 \n 193.0 \n 0.279232 \n 0.626080 \n 0.115140 \n \n \n 191 \n 192 \n 0.0001 \n 0.504240 \n 0.241419 \n 146.75 \n 128.0 \n 180.0 \n 0.00 \n 146.750000 \n 128.0 \n 180.0 \n 0.082597 \n 0.504240 \n 0.241419 \n \n \n 192 \n 193 \n 0.0001 \n 0.679015 \n 0.104637 \n 159.75 \n 116.0 \n 208.0 \n 0.00 \n 159.750000 \n 116.0 \n 208.0 \n 0.079804 \n 0.679015 \n 0.104637 \n \n \n 193 \n 194 \n 0.0002 \n 0.409415 \n 0.031267 \n 188.00 \n 129.0 \n 346.0 \n 0.00 \n 188.000000 \n 129.0 \n 346.0 \n 0.181960 \n 0.409415 \n 0.031267 \n \n \n 194 \n 195 \n 0.0001 \n 0.731575 \n 0.089468 \n 214.25 \n 117.0 \n 400.0 \n 0.25 \n 152.333344 \n 117.0 \n 210.0 \n 0.060617 \n 0.731575 \n 0.089468 \n \n \n 195 \n 196 \n 0.0001 \n 0.505975 \n 0.085279 \n 133.00 \n 110.0 \n 146.0 \n 0.00 \n 133.000000 \n 110.0 \n 146.0 \n 0.076874 \n 0.505975 \n 0.085279 \n \n \n 196 \n 197 \n 0.0001 \n 0.574080 \n 0.020738 \n 151.75 \n 142.0 \n 160.0 \n 0.00 \n 151.750000 \n 142.0 \n 160.0 \n 0.090732 \n 0.574080 \n 0.020738 \n \n \n 197 \n 198 \n 0.0001 \n 0.665815 \n 0.115322 \n 142.25 \n 113.0 \n 178.0 \n 0.00 \n 142.250000 \n 113.0 \n 178.0 \n 0.088252 \n 0.665815 \n 0.115322 \n \n \n 198 \n 199 \n 0.0001 \n 0.625250 \n 0.094672 \n 205.50 \n 118.0 \n 337.0 \n 0.00 \n 205.500000 \n 118.0 \n 337.0 \n 0.077750 \n 0.625250 \n 0.094672 \n \n \n 199 \n 200 \n 0.0001 \n 0.398875 \n 0.033887 \n 194.75 \n 92.0 \n 400.0 \n 0.25 \n 126.333336 \n 92.0 \n 150.0 \n 0.104137 \n 0.398875 \n 0.033887 \n \n \n
\n
\n\n
\n \n \n Step \n Training Loss \n rewards / chosen \n rewards / rejected \n rewards / accuracies \n rewards / margins \n logps / chosen \n logps / rejected \n logits / chosen \n logits / rejected \n \n \n \n \n 0 \n 5 \n 0.6089 \n 1.455713 \n 1.231354 \n 0.650000 \n 0.224359 \n -59.668617 \n -43.956375 \n -1.184955 \n -1.328339 \n \n \n 1 \n 10 \n 0.6052 \n 1.526210 \n 1.281936 \n 0.705882 \n 0.244274 \n -58.097687 \n -42.015732 \n -1.182442 \n -1.327034 \n \n \n 2 \n 15 \n 0.5891 \n 1.395292 \n 1.212803 \n 0.764706 \n 0.182489 \n -59.745697 \n -43.838898 \n -1.175694 \n -1.318733 \n \n \n 3 \n 20 \n 0.5252 \n 1.607121 \n 1.198129 \n 0.823529 \n 0.408992 \n -59.078156 \n -44.294483 \n -1.193994 \n -1.331355 \n \n \n 4 \n 25 \n 0.4986 \n 1.628060 \n 1.198231 \n 0.882353 \n 0.429830 \n -59.343346 \n -42.700844 \n -1.175691 \n -1.355025 \n \n \n 5 \n 30 \n 0.5832 \n 1.582687 \n 1.223329 \n 0.764706 \n 0.359358 \n -59.076710 \n -43.675716 \n -1.183481 \n -1.317861 \n \n \n 6 \n 35 \n 0.5294 \n 1.626609 \n 1.217587 \n 0.800000 \n 0.409021 \n -59.163441 \n -44.007710 \n -1.179030 \n -1.333033 \n \n \n 7 \n 40 \n 0.4710 \n 1.659547 \n 1.158330 \n 0.882353 \n 0.501216 \n -58.059547 \n -41.517879 \n -1.183498 \n -1.346062 \n \n \n 8 \n 45 \n 0.5716 \n 1.486256 \n 1.176512 \n 0.705882 \n 0.309744 \n -55.760204 \n -46.438583 \n -1.207841 \n -1.315339 \n \n \n 9 \n 50 \n 0.4522 \n 1.819323 \n 1.275259 \n 0.823529 \n 0.544064 \n -56.173222 \n -42.443119 \n -1.189783 \n -1.355598 \n \n \n
\n
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\n"},"metadata":{}},{"name":"stdout","text":"\n=== GRPO ===\nsteps 1–200 (n=200)\ncombined μ=0.5066 | task μ=0.6292 | belief μ=0.2786\n(task/belief split is approximate when only `reward` is present)\nclipped batches: 20/200\n\n=== DPO ===\nsteps 5–50 (n=10)\naccuracy 0.650 → 0.824\nmargin 0.2244 → 0.5441\n","output_type":"stream"}],"execution_count":11},{"cell_type":"code","source":"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-28T15:15:54.894495Z","iopub.execute_input":"2026-04-28T15:15:54.895419Z","iopub.status.idle":"2026-04-28T15:15:55.083276Z","shell.execute_reply.started":"2026-04-28T15:15:54.895373Z","shell.execute_reply":"2026-04-28T15:15:55.082197Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}