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ML Debug Env — GRPO Training Notebook\n\n**Instructions:**\n1. Runtime → Change runtime type → T4 GPU\n2. In Cell 2, replace `your_groq_api_key_here` with your Groq API key (free at console.groq.com)\n3. Run all cells top to bottom (Runtime → Run all)\n\n**Expected output:** 200 training steps with reward improving from ~0.024 to ~0.190","metadata":{}},{"cell_type":"code","source":"# CELL 1 — Install\n!pip install -q unsloth\n!pip install -q \"transformers==4.46.3\" --force-reinstall\n!pip install -q \"trl>=0.15.0\" \"datasets\" \"openai\" \"httpx\" \"matplotlib\" \"peft\" \"accelerate\"\nimport importlib\nimport torch, transformers, trl, unsloth, peft\nprint(f\"torch: {torch.__version__}\")\nprint(f\"transformers: {transformers.__version__}\")\nprint(f\"trl: {trl.__version__}\")\nprint(f\"unsloth: {unsloth.__version__}\")\nprint(f\"peft: {peft.__version__}\")\nprint(f\"GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'NO GPU'}\")\nprint(\"Done\")","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NzCpPiOmjrnf","outputId":"fd092f88-b39f-4db4-e947-e3c2c32bdb21","trusted":true,"execution":{"iopub.status.busy":"2026-04-26T03:40:25.841489Z","iopub.execute_input":"2026-04-26T03:40:25.842253Z","iopub.status.idle":"2026-04-26T03:41:35.887616Z","shell.execute_reply.started":"2026-04-26T03:40:25.842223Z","shell.execute_reply":"2026-04-26T03:41:35.886708Z"}},"outputs":[{"name":"stdout","text":"\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.\ngoogle-adk 1.25.1 requires google-cloud-bigquery-storage>=2.0.0, which is not installed.\ntrl 0.24.0 requires transformers>=4.56.1, but you have transformers 4.46.3 which is incompatible.\nunsloth-zoo 2026.4.9 requires transformers!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0,!=4.54.0,!=4.55.0,!=4.55.1,!=4.57.4,!=4.57.5,!=5.0.0,!=5.1.0,<=5.5.0,>=4.51.3, but you have transformers 4.46.3 which is incompatible.\nunsloth 2026.4.8 requires transformers!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0,!=4.54.0,!=4.55.0,!=4.55.1,!=4.57.0,!=4.57.4,!=4.57.5,!=5.0.0,!=5.1.0,<=5.5.0,>=4.51.3, but you have transformers 4.46.3 which is incompatible.\ndatasets 4.3.0 requires fsspec[http]<=2025.9.0,>=2023.1.0, but you have fsspec 2026.3.0 which is incompatible.\ns3fs 2026.2.0 requires fsspec==2026.2.0, but you have fsspec 2026.3.0 which is incompatible.\nydata-profiling 4.18.1 requires numpy<2.4,>=1.22, but you have numpy 2.4.4 which is incompatible.\ngoogle-colab 1.0.0 requires jupyter-server==2.14.0, but you have jupyter-server 2.12.5 which is incompatible.\ngoogle-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.3 which is incompatible.\ngoogle-colab 1.0.0 requires requests==2.32.4, but you have requests 2.33.1 which is incompatible.\ndopamine-rl 4.1.2 requires gym<=0.25.2, but you have gym 0.26.2 which is incompatible.\ntensorflow 2.19.0 requires numpy<2.2.0,>=1.26.0, but you have numpy 2.4.4 which is incompatible.\nnumba 0.60.0 requires numpy<2.1,>=1.22, but you have numpy 2.4.4 which is incompatible.\ngcsfs 2025.3.0 requires fsspec==2025.3.0, but you have fsspec 2026.3.0 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0m\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.\nunsloth-zoo 2026.4.9 requires transformers!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0,!=4.54.0,!=4.55.0,!=4.55.1,!=4.57.4,!=4.57.5,!=5.0.0,!=5.1.0,<=5.5.0,>=4.51.3, but you have transformers 5.6.2 which is incompatible.\nunsloth 2026.4.8 requires transformers!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0,!=4.54.0,!=4.55.0,!=4.55.1,!=4.57.0,!=4.57.4,!=4.57.5,!=5.0.0,!=5.1.0,<=5.5.0,>=4.51.3, but you have transformers 5.6.2 which is incompatible.\ns3fs 2026.2.0 requires fsspec==2026.2.0, but you have fsspec 2025.9.0 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","output_type":"stream"},{"name":"stderr","text":"/tmp/ipykernel_157/2151878775.py:6: UserWarning: WARNING: Unsloth should be imported before [trl, transformers] to ensure all optimizations are applied. Your code may run slower or encounter memory issues without these optimizations.\n\nPlease restructure your imports with 'import unsloth' at the top of your file.\n  import torch, transformers, trl, unsloth, peft\n","output_type":"stream"},{"name":"stdout","text":"🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n🦥 Unsloth Zoo will now patch everything to make training faster!\ntorch: 2.10.0+cu128\ntransformers: 5.6.2\ntrl: 0.24.0\nunsloth: 2026.4.8\npeft: 0.18.1\nGPU: Tesla T4\nDone\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"# CELL 2 — Clone repo and setup\nimport os, sys\n\n!git clone https://github.com/RAK2315/ml-debug-env.git\nos.chdir('ml-debug-env')\nsys.path.insert(0, 'server')\nsys.path.insert(0, '.')\n\nos.environ['GROQ_API_KEY'] = os.environ['GROQ_API_KEY'] = 'your_groq_api_key_here'\nos.environ['PYTHON_EXEC'] = '/usr/bin/python3'\n\nprint('Working dir:', os.getcwd())\nprint('Done')","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1GwVw9N7j2MT","outputId":"f8189b33-33f5-4fee-a53e-7282a12b603d","trusted":true,"execution":{"iopub.status.busy":"2026-04-26T03:41:35.889295Z","iopub.execute_input":"2026-04-26T03:41:35.890043Z","iopub.status.idle":"2026-04-26T03:41:36.642183Z","shell.execute_reply.started":"2026-04-26T03:41:35.889973Z","shell.execute_reply":"2026-04-26T03:41:36.641399Z"}},"outputs":[{"name":"stdout","text":"Cloning into 'ml-debug-env'...\nremote: Enumerating objects: 255, done.\u001b[K\nremote: Counting objects: 100% (255/255), done.\u001b[K\nremote: Compressing objects: 100% (165/165), done.\u001b[K\nremote: Total 255 (delta 133), reused 207 (delta 85), pack-reused 0 (from 0)\u001b[K\nReceiving objects: 100% (255/255), 746.94 KiB | 7.05 MiB/s, done.\nResolving deltas: 100% (133/133), done.\nWorking dir: /kaggle/working/ml-debug-env\nDone\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"# CELL 3 — Test environment imports\nfrom bug_generator import get_scenario, execute_tool, ALL_TASKS, AVAILABLE_TOOLS\nfrom grader import grade\n\ns = get_scenario('shape_mismatch', seed=42)\nprint('Alert:', s.alert)\nprint('Tasks:', ALL_TASKS)\nprint('Tools:', AVAILABLE_TOOLS)\nprint('Done')","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QcaM5zorki-S","outputId":"6ddaae08-dbdb-4062-b63a-b1c50548f39e","trusted":true,"execution":{"iopub.status.busy":"2026-04-26T03:41:36.643553Z","iopub.execute_input":"2026-04-26T03:41:36.644197Z","iopub.status.idle":"2026-04-26T03:41:36.662774Z","shell.execute_reply.started":"2026-04-26T03:41:36.644149Z","shell.execute_reply":"2026-04-26T03:41:36.661961Z"}},"outputs":[{"name":"stdout","text":"Alert: Training job crashed immediately. No epochs completed. Exit code 1.\nTasks: ['shape_mismatch', 'training_collapse', 'data_leakage', 'wrong_device', 'gradient_not_zeroed', 'missing_eval_mode', 'compound_shape_device', 'compound_leakage_eval']\nTools: ['run_code', 'get_traceback', 'inspect_gradients', 'print_shapes', 'view_source']\nDone\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"# CELL 4 — Load model with Unsloth\nimport unsloth\nfrom unsloth import FastLanguageModel\nimport torch\n\nmodel, tokenizer = FastLanguageModel.from_pretrained(\n    model_name='Qwen/Qwen2.5-1.5B-Instruct',\n    max_seq_length=1024,\n    load_in_4bit=True,\n    dtype=None,\n)\n\nmodel = FastLanguageModel.get_peft_model(\n    model,\n    r=16,\n    target_modules=['q_proj', 'v_proj', 'k_proj', 'o_proj'],\n    lora_alpha=16,\n    lora_dropout=0,\n    bias='none',\n    use_gradient_checkpointing='unsloth',\n    random_state=42,\n)\n\nmodel.print_trainable_parameters()\nprint('Model loaded OK')","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":545,"referenced_widgets":["2281eac9ef8a46ffbeaa1e229377a1f7","3d634c60c81d4194918b56a4f8afe3b2","b5127ee2eac047789a3e273178edabb0","3c6d2f3fd9be4a53ba58906dad5eac4d","8fbde1f763394557ba96f75bc40cd30c","0d0a298d000444b3888bec66a571aacc","15c6f12d601a467189cb64a636698e6d","67665880a6064d8aa15b56f8b41da4ce","1a54e4d064a84acb94afd70ff3a4b6dd","5895a075e132485dbfbb2672fba2c6f3","2a74cdc749d04eca9a35b9d2b4ee95a4","bd1764e685004071b3b7e2fb3954354d","89692451784a46e882fc4891805cf94b","2443526357874e1eb5433d45bf997726","318304f734e548bd940799a0114bbea4","dda4f63f67824e9cbd1190518e3430ef","bd7160de08bc43cc8b5ab9f951689d3c","bc846758b562496881efeaa53e5a65d0","89a86b7ec7cd4f11bce6cfdf74a74859","0033784e76a14c499a11afe52d5663bd","9b525341caac4d799c5d4ecc25529f38","1215e46bcc1441d8a44f625f87c21ca4","c764d1e30c824cb59aef070fbb42cdfe","b17a082af90241b3bd243bda2d973c82","07997324c5744865a884c01649c1cc27","72162ac3f90049ae9a724d6ab28e1380","7e286ba6d9274f2bb47c989ade4551c2","67e94da6868f4490b8121a57b672fecd","b7a63551b1ba4ec4a1d38f1b8c600e70","071320254df3489cba786752fece99db","8136eeed59074f8a88c3a0a002cc0f82","95f14457e5e64e979e9d16bceb682eee","80f1b1e3d90748c3bd50007e64d6dce2","496021df83e540979b40bfd77e40bcc5","1b6b6a1ae2b94cabb633ba31a07916f3","194ac0f3efd5412d90de08506ce07d4d","6514f2f4319d49fc8145894360ef0a00","d70d05f43c614000a549f18fa6115f56","f13406abc9f04768b63c90d9cce9327a","8fbeefbf17c94c59a99f6712131fa8e0","3fbb766ff3cf4a7dafefe60448316e4b","d19a23520d834b18be1566df416c6ca7","673161fc3c6d4338b51675eb4ed2d910","695435a638444ba1b59d9454f4f535ed","d1c7c9693c18404da00a03d09bcc3690","1ad092e2b7e545f1abb9db79f44c271f","daaa95d2cfdc485eae6f7378ba3927f8","01ae29df97c74975a9021302a1b7175c","2035ada5e7974680953e1fa92fbab19f","cf63ee7bc5284cefa93f24a7b0194fd5","cc6c7c08342541deadbd5126f06a4d13","33b14bf60ba14ffab12b6717db9dd44d","95fae661638148ccbfbe93cdc6c89e41","e75dd41fde8144d58257286e6d192430","dcf27f1a57f348e4b873ac8f212a7f43","98754a8cc2404500b6870ef40e0bfc21","f743bede63bf4279a453c1c8f7472d30","3dae6cc98e5049ffa28caf9b53960797","c238c3ff7a5c43798a344fd684697ff7","56391d9c93f64ca3ad637147353fc431","c7266dfd0b9843e5b2c621775628eb20","b6c59774349841f0a9c4cdd637087b5a","16025c837b9a417bb3cce98d149850a1","bbbe5054fc5148a3bbe6b5b5f3f8b990","fe7fda310a96423ca69db6452eaeec01","229b7b2263174b9c91482f2dce5ee59c","5489c67953c84d15a2642e20e8abdec4","1a99edeb96ad4ba2bcf5e531ed3fb7aa","b8d7516bb2b647b0bd4de17fa3120215","55f5e9e182d84eeabc4d6cd7074dac6e","14a24be33b84407c925f4c813752749a","b05e087b4b0844fe833c447aacbae11d","8fbe65d7d8034056a4fbe8e1076949cb","c89c4554ba93494aad15f4924d017e35","8513a8a63f84484eb9ac797cc13ff5df","d773073617c44643b78cae70cc68096b","587a9bd88155444487feb0526c537d87","af8052d2f135494391ca75ede332bfde","5722f35746224a818993cccce63147a2","157647823866406da03f5e8b36fd1cc7","5da1729fa3ab48358ff5c11b795b30d7","602d1d8b648e4c21a556035d19582a9e","b12a6ee47c59457e9c520be163176173","299d0a9b206049cca860ceeaaa4e84e3","36f9ab53348042759da0db74078eeedf","0c7bad2afa3340a69ecc5347467008b9","62cf96cbce144385a4a7f8f8d5c9e3f0","6f181a2eda554939a3269f12cc5cbff7","cd2c076f648345aa9dba489d7870d4b6","73bee939f8fc4dabbe1a61147f431450","0d0d1bfa91e045c9bf21df126d719d90","9b1c9dbf42aa44bf9d840f336475a940","6dd043246c154c69ac560b75457ab074","9ebe2c0550f445fbb8ae1030caf6c037","6a0d5a857c884f5db2f3e2cb75082394","ae523e120df348c8932c6734cd45a788","62cdc4c1742343139a7f412416e3ae62","1577165b69f14efdbbbf010cc7d93320","b72ac3e01f5b40a6a033cbc6f4d85656","97628ca4ba6c4608882e451084a42f91","849aadd72ffe4d5f86ffd64d5b631f57","2ae5e0f58c874bf78cbe3fa3b6d0d4dd","1388ebdd52ed475ba419bb0d9fae48ef","0faf2082ab86445fa85e40897e9c4ea9","0281d177273a4d6cb4ab1b883dbd8798","5d6db69cd8064b8193643a362f9d706d","7c83d14061f7463cadab215fc83f58b2","1478e95fc07344cda561c5f0c09ea8d3","2ef6f6cb02ce4895a63b2d6a50d373cb","86f434e914724c4da65b7ac0e1069928"]},"id":"SFyhhi5IkoZp","outputId":"bf03f0bd-0b81-4b12-ff55-058d092e2225","trusted":true,"execution":{"iopub.status.busy":"2026-04-26T03:41:36.663879Z","iopub.execute_input":"2026-04-26T03:41:36.664398Z","iopub.status.idle":"2026-04-26T03:41:58.387748Z","shell.execute_reply.started":"2026-04-26T03:41:36.664359Z","shell.execute_reply":"2026-04-26T03:41:58.386917Z"}},"outputs":[{"name":"stdout","text":"==((====))==  Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 5.6.2.\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 = 0.0.35. 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:   0%|          | 0.00/1.53G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2fcf205dbd084a96809d884e0dcd28a1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading weights:   0%|          | 0/338 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5bc525a59298440782b107222f9f36c3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"generation_config.json:   0%|          | 0.00/270 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8aa241eccbce43deb51efcedfd26fe21"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"61cee3f1a91741b890383f63c26b2f88"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1e8c7097d96548c0a40dc20560d18dca"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocab.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"edc70fb386d2445da3ec75926299488c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"merges.txt: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d70158e76e0a4d5786ea105c959a2678"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0799ded7b7c549ae9e997eb6a164e68c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"added_tokens.json:   0%|          | 0.00/605 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8857cae2087443cab1945d3eaea487ed"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/614 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7167438354a7457db83f686589d1cc82"}},"metadata":{}},{"name":"stdout","text":"unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit does not have a padding token! Will use pad_token = <|PAD_TOKEN|>.\n","output_type":"stream"},{"name":"stderr","text":"[transformers] Not an error, but Unsloth cannot patch MLP layers with our manual autograd engine since either LoRA adapters\nare not enabled or a bias term (like in Qwen) is used.\n[transformers] Unsloth 2026.4.8 patched 28 layers with 28 QKV layers, 28 O layers and 0 MLP layers.\n","output_type":"stream"},{"name":"stdout","text":"trainable params: 4,358,144 || all params: 1,548,072,448 || trainable%: 0.2815\nModel loaded OK\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"# CELL 5 — System prompt, reward function, parse_action\nimport json\n\nTRAIN_SYSTEM = \"\"\"You are an expert ML engineer. Debug the broken PyTorch script.\nRespond with JSON only:\n{\"bug_type\": \"<shape_mismatch|training_collapse|data_leakage|wrong_device|gradient_not_zeroed|missing_eval_mode|other>\", \"diagnosis\": \"<explanation>\", \"fixed_code\": \"<complete fixed script>\"}\nNo markdown. No text outside JSON.\"\"\"\n\ndef make_train_prompt(task_id, seed=42):\n    scenario = get_scenario(task_id, seed=seed)\n    return [\n        {\"role\": \"system\", \"content\": TRAIN_SYSTEM},\n        {\"role\": \"user\", \"content\": (\n            f\"Broken script:\\n```python\\n{scenario.buggy_code}\\n```\\n\\n\"\n            f\"Error/failure:\\n{scenario.error_output}\\n\\n\"\n            f\"Return JSON fix only.\"\n        )}\n    ], scenario\n\ndef parse_action(text):\n    text = text.strip()\n    # try direct parse first\n    try:\n        return json.loads(text)\n    except:\n        pass\n    # strip markdown fences\n    if '```' in text:\n        parts = text.split('```')\n        for part in parts:\n            part = part.strip()\n            if part.startswith('json'):\n                part = part[4:].strip()\n            if part.startswith('{'):\n                try:\n                    return json.loads(part)\n                except:\n                    continue\n    # find first { ... } block\n    try:\n        start = text.index('{')\n        end = text.rindex('}') + 1\n        return json.loads(text[start:end])\n    except:\n        pass\n    return {}\n\ndef compute_reward(completion, task_id, seed=42):\n    try:\n        parsed = parse_action(completion)\n        action_type = parsed.get('action_type', 'fix')\n        if action_type == 'inspect':\n            return 0.0\n        bug_type = parsed.get('bug_type', 'other')\n        diagnosis = parsed.get('diagnosis', '')\n        fixed_code = parsed.get('fixed_code', '')\n        if not fixed_code or len(fixed_code) < 50:\n            return 0.0\n        scenario = get_scenario(task_id, seed=seed)\n        result = grade(\n            action_bug_type=bug_type,\n            action_diagnosis=diagnosis,\n            fixed_code=fixed_code,\n            scenario=scenario,\n        )\n        return float(result.score)\n    except Exception as e:\n        print(f\"  [reward error] {e}\")\n        return 0.0\n\n# Quick test\nmessages, scenario = make_train_prompt('shape_mismatch', seed=42)\nprompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\nprint('Prompt length:', len(prompt))\nprint('Done')","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0EyIvFcGkvaH","outputId":"d173ce48-3791-4d7c-99bb-a85219aead8f","trusted":true,"execution":{"iopub.status.busy":"2026-04-26T03:41:58.389697Z","iopub.execute_input":"2026-04-26T03:41:58.390612Z","iopub.status.idle":"2026-04-26T03:41:58.401925Z","shell.execute_reply.started":"2026-04-26T03:41:58.390521Z","shell.execute_reply":"2026-04-26T03:41:58.401135Z"}},"outputs":[{"name":"stdout","text":"Prompt length: 1598\nDone\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"import warnings\nwarnings.filterwarnings('ignore')","metadata":{"id":"gzGzNfwPEJCr","trusted":true,"execution":{"iopub.status.busy":"2026-04-26T03:41:58.402856Z","iopub.execute_input":"2026-04-26T03:41:58.404032Z","iopub.status.idle":"2026-04-26T03:41:58.431016Z","shell.execute_reply.started":"2026-04-26T03:41:58.403947Z","shell.execute_reply":"2026-04-26T03:41:58.430305Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"# CELL 6 — GRPO training loop (fixed)\nimport warnings\nwarnings.filterwarnings('ignore')\nimport logging\nlogging.getLogger('transformers').setLevel(logging.ERROR)\nimport torch\nfrom torch.optim import AdamW\nimport random\n\noptimizer = AdamW(model.parameters(), lr=5e-6)\nreward_log = []\nMAX_STEPS = 200\nNUM_GENERATIONS = 4\nMIN_COMPLETION_CHARS = 50   # was 100\nMIN_REWARD = 0.015          # was 0.05\n\nTRAIN_TASKS = ['shape_mismatch', 'training_collapse', 'wrong_device', 'gradient_not_zeroed']\n\ndef generate_completion(messages):\n    prompt = tokenizer.apply_chat_template(\n        messages, tokenize=False, add_generation_prompt=True\n    )\n    inputs = tokenizer(prompt, return_tensors='pt', truncation=True, max_length=512).to('cuda')\n    with torch.no_grad():\n        output = model.generate(\n            **inputs,\n            max_new_tokens=1000,\n            do_sample=True,\n            temperature=0.9,\n            pad_token_id=tokenizer.eos_token_id,\n        )\n    return tokenizer.decode(output[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)\n\ndataset = []\nfor i in range(50):\n    for task_id in TRAIN_TASKS:\n        dataset.append({'task_id': task_id, 'seed': i})\nrandom.shuffle(dataset)\n\nprompt_text_cache = {}\n\nstep = 0\nskipped = 0\nfor episode in dataset:\n    if step >= MAX_STEPS:\n        break\n\n    task_id = episode['task_id']\n    seed = episode['seed']\n    messages, scenario = make_train_prompt(task_id, seed=seed)\n\n    completions, rewards = [], []\n    for _ in range(NUM_GENERATIONS):\n        completion = generate_completion(messages)\n        # Skip short/garbage outputs — do not train on them\n        if len(completion.strip()) < MIN_COMPLETION_CHARS:\n            skipped += 1\n            continue\n        r = compute_reward(completion, task_id, seed=seed)\n        # Skip wrong-bug-type noise\n        if r <= MIN_REWARD:\n            skipped += 1\n            continue\n        completions.append(completion)\n        rewards.append(r)\n\n    if len(rewards) < 2:\n        # Not enough valid completions to compute meaningful advantage\n        avg_reward = float(sum(rewards) / len(rewards)) if rewards else 0.0\n        reward_log.append({'step': step, 'reward': avg_reward})\n        print(f'Step {step:3d} | {task_id:25s} | skipped (valid={len(rewards)}) | avg={avg_reward:.3f}')\n        step += 1\n        continue\n\n    rewards_tensor = torch.tensor(rewards, dtype=torch.float32)\n    avg_reward = float(rewards_tensor.mean())\n    reward_log.append({'step': step, 'reward': avg_reward})\n\n    if rewards_tensor.std() < 1e-6:\n        print(f'Step {step:3d} | {task_id:25s} | {[f\"{r:.2f}\" for r in rewards]} | avg={avg_reward:.3f} [no variance]')\n        step += 1\n        continue\n\n    mean_r = rewards_tensor.mean()\n    std_r = rewards_tensor.std() + 1e-8\n    advantages = (rewards_tensor - mean_r) / std_r\n\n    prompt_text = tokenizer.apply_chat_template(\n        messages, tokenize=False, add_generation_prompt=True\n    )\n\n    # Train on ALL valid completions weighted by their advantage (proper GRPO)\n    model.train()\n    total_loss = torch.tensor(0.0, device='cuda')\n    trained_on = 0\n    for i, (completion, adv) in enumerate(zip(completions, advantages.tolist())):\n        full_text = prompt_text + completion\n        inputs = tokenizer(\n            full_text, return_tensors='pt', truncation=True, max_length=1500\n        ).to('cuda')\n        outputs = model(**inputs, labels=inputs['input_ids'])\n        total_loss = total_loss + (-adv * outputs.loss)\n        trained_on += 1\n\n    if trained_on > 0:\n        loss = total_loss / trained_on\n        optimizer.zero_grad()\n        loss.backward()\n        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n        optimizer.step()\n\n    print(f'Step {step:3d} | {task_id:25s} | {[\"{:.2f}\".format(r) for r in rewards]} | avg={avg_reward:.3f}')\n    step += 1\n\nprint(f'Done. Steps: {step}, total skipped generations: {skipped}')\n\n\nimport json\nwith open('reward_log.json', 'w') as f:\n    json.dump(reward_log, f)\nprint(f'Saved {len(reward_log)} steps to reward_log.json')\n","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wsHmHWsbk_SC","outputId":"624bdebc-f2db-49df-bc9d-951b6b9d486b","trusted":true,"execution":{"iopub.status.busy":"2026-04-26T03:41:58.432084Z","iopub.execute_input":"2026-04-26T03:41:58.432878Z","iopub.status.idle":"2026-04-26T05:38:17.383836Z","shell.execute_reply.started":"2026-04-26T03:41:58.432845Z","shell.execute_reply":"2026-04-26T05:38:17.382876Z"}},"outputs":[{"name":"stdout","text":"Step   0 | wrong_device              | ['0.26', '0.32', '0.23', '0.26'] | avg=0.267\nStep   1 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep   2 | training_collapse         | ['0.20', '0.20', '0.20'] | avg=0.200 [no variance]\nUnsloth: Will smartly offload gradients to save VRAM!\nStep   3 | training_collapse         | ['0.29', '0.99', '0.29'] | avg=0.523\nStep   4 | gradient_not_zeroed       | ['0.20', '0.20'] | avg=0.200 [no variance]\nStep   5 | training_collapse         | ['0.29', '0.26'] | avg=0.275\nStep   6 | training_collapse         | skipped (valid=1) | avg=0.200\nStep   7 | wrong_device              | ['0.30', '0.26'] | avg=0.282\nStep   8 | shape_mismatch            | ['0.32', '0.20'] | avg=0.260\nStep   9 | shape_mismatch            | ['0.35', '0.24', '0.40'] | avg=0.332\nStep  10 | shape_mismatch            | ['0.20', '0.20', '0.20', '0.99'] | avg=0.398\nStep  11 | wrong_device              | ['0.20', '0.26'] | avg=0.230\nStep  12 | shape_mismatch            | skipped (valid=1) | avg=0.400\nStep  13 | shape_mismatch            | ['0.40', '0.20'] | avg=0.300\nStep  14 | wrong_device              | ['0.20', '0.20'] | avg=0.200 [no variance]\nStep  15 | gradient_not_zeroed       | ['0.20', '0.26'] | avg=0.230\nStep  16 | training_collapse         | skipped (valid=1) | avg=0.290\nStep  17 | wrong_device              | ['0.26', '0.51'] | avg=0.382\nStep  18 | wrong_device              | skipped (valid=1) | avg=0.260\nStep  19 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep  20 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep  21 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep  22 | wrong_device              | ['0.26', '0.26'] | avg=0.260 [no variance]\nStep  23 | wrong_device              | ['0.30', '0.23'] | avg=0.268\nStep  24 | wrong_device              | skipped (valid=1) | avg=0.275\nStep  25 | wrong_device              | skipped (valid=1) | avg=0.260\nStep  26 | gradient_not_zeroed       | skipped (valid=1) | avg=0.260\nStep  27 | wrong_device              | skipped (valid=1) | avg=0.200\nStep  28 | training_collapse         | ['0.20', '0.20', '0.20'] | avg=0.200 [no variance]\nStep  29 | wrong_device              | skipped (valid=0) | avg=0.000\nStep  30 | training_collapse         | skipped (valid=1) | avg=0.335\nStep  31 | wrong_device              | ['0.28', '0.46'] | avg=0.368\nStep  32 | shape_mismatch            | ['0.26', '0.43', '0.46'] | avg=0.383\nStep  33 | shape_mismatch            | ['0.26', '0.20', '0.20'] | avg=0.220\nStep  34 | training_collapse         | skipped (valid=1) | avg=0.230\nStep  35 | training_collapse         | skipped (valid=1) | avg=0.290\nStep  36 | training_collapse         | ['0.20', '0.40'] | avg=0.300\nStep  37 | gradient_not_zeroed       | skipped (valid=1) | avg=0.275\nStep  38 | shape_mismatch            | ['0.40', '0.51', '0.26'] | avg=0.388\nStep  39 | shape_mismatch            | ['0.40', '0.20'] | avg=0.300\nStep  40 | gradient_not_zeroed       | skipped (valid=1) | avg=0.200\nStep  41 | shape_mismatch            | ['0.40', '0.32'] | avg=0.360\nStep  42 | wrong_device              | ['0.28', '0.26', '0.46'] | avg=0.332\nStep  43 | shape_mismatch            | ['0.43', '0.35'] | avg=0.390\nStep  44 | shape_mismatch            | ['0.26', '0.29', '0.43', '0.35'] | avg=0.332\nStep  45 | shape_mismatch            | ['0.32', '0.43', '0.26'] | avg=0.337\nStep  46 | training_collapse         | skipped (valid=1) | avg=0.350\nStep  47 | wrong_device              | ['0.32', '0.20'] | avg=0.260\nStep  48 | training_collapse         | skipped (valid=1) | avg=0.400\nStep  49 | training_collapse         | ['0.26', '0.26', '0.20'] | avg=0.240\nStep  50 | training_collapse         | ['0.20', '0.40'] | avg=0.300\nStep  51 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep  52 | gradient_not_zeroed       | skipped (valid=1) | avg=0.200\nStep  53 | training_collapse         | ['0.20', '0.40'] | avg=0.300\nStep  54 | gradient_not_zeroed       | skipped (valid=1) | avg=0.275\nStep  55 | shape_mismatch            | ['0.46', '0.43', '0.32'] | avg=0.403\nStep  56 | training_collapse         | ['0.20', '0.20'] | avg=0.200 [no variance]\nStep  57 | shape_mismatch            | ['0.40', '0.26', '0.20'] | avg=0.287\nStep  58 | shape_mismatch            | ['0.40', '0.40'] | avg=0.400 [no variance]\nStep  59 | shape_mismatch            | ['0.52', '0.24'] | avg=0.382\nStep  60 | wrong_device              | skipped (valid=1) | avg=0.200\nStep  61 | wrong_device              | skipped (valid=1) | avg=0.275\nStep  62 | shape_mismatch            | ['0.23', '0.35', '0.49'] | avg=0.357\nStep  63 | shape_mismatch            | ['0.30', '0.46', '0.40'] | avg=0.388\nStep  64 | gradient_not_zeroed       | skipped (valid=1) | avg=0.305\nStep  65 | wrong_device              | skipped (valid=0) | avg=0.000\nStep  66 | gradient_not_zeroed       | ['0.75', '0.26', '0.34', '0.35'] | avg=0.424\nStep  67 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep  68 | wrong_device              | ['0.28', '0.23'] | avg=0.252\nStep  69 | training_collapse         | ['0.20', '0.20'] | avg=0.200 [no variance]\nStep  70 | wrong_device              | ['0.30', '0.26'] | avg=0.282\nStep  71 | gradient_not_zeroed       | ['0.30', '0.20'] | avg=0.252\nStep  72 | training_collapse         | skipped (valid=1) | avg=0.275\nStep  73 | shape_mismatch            | skipped (valid=1) | avg=0.320\nStep  74 | training_collapse         | skipped (valid=1) | avg=0.350\nStep  75 | wrong_device              | ['0.24', '0.23'] | avg=0.238\nStep  76 | shape_mismatch            | ['0.23', '0.46', '0.46', '0.23'] | avg=0.345\nStep  77 | gradient_not_zeroed       | skipped (valid=1) | avg=0.230\nStep  78 | wrong_device              | skipped (valid=0) | avg=0.000\nStep  79 | shape_mismatch            | skipped (valid=1) | avg=0.320\nStep  80 | training_collapse         | skipped (valid=1) | avg=0.320\nStep  81 | wrong_device              | ['0.26', '0.28', '0.26'] | avg=0.265\nStep  82 | training_collapse         | ['0.99', '0.23'] | avg=0.610\nStep  83 | training_collapse         | ['0.40', '0.20', '0.40'] | avg=0.333\nStep  84 | wrong_device              | skipped (valid=1) | avg=0.245\nStep  85 | training_collapse         | skipped (valid=0) | avg=0.000\nStep  86 | wrong_device              | skipped (valid=1) | avg=0.260\nStep  87 | shape_mismatch            | ['0.24', '0.23', '0.45'] | avg=0.307\nStep  88 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep  89 | gradient_not_zeroed       | skipped (valid=1) | avg=0.200\nStep  90 | gradient_not_zeroed       | skipped (valid=1) | avg=0.260\nStep  91 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep  92 | shape_mismatch            | ['0.23', '0.23', '0.20'] | avg=0.220\nStep  93 | shape_mismatch            | ['0.26', '0.35'] | avg=0.305\nStep  94 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep  95 | shape_mismatch            | ['0.51', '0.20'] | avg=0.352\nStep  96 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep  97 | shape_mismatch            | skipped (valid=1) | avg=0.350\nStep  98 | shape_mismatch            | ['0.51', '0.43'] | avg=0.468\nStep  99 | gradient_not_zeroed       | skipped (valid=1) | avg=0.200\nStep 100 | wrong_device              | skipped (valid=1) | avg=0.260\nStep 101 | shape_mismatch            | skipped (valid=1) | avg=0.505\nStep 102 | training_collapse         | ['0.20', '0.40'] | avg=0.300\nStep 103 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep 104 | gradient_not_zeroed       | skipped (valid=1) | avg=0.305\nStep 105 | wrong_device              | skipped (valid=0) | avg=0.000\nStep 106 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep 107 | shape_mismatch            | ['0.55', '0.30'] | avg=0.428\nStep 108 | wrong_device              | ['0.26', '0.23', '0.20'] | avg=0.230\nStep 109 | wrong_device              | skipped (valid=1) | avg=0.305\nStep 110 | wrong_device              | skipped (valid=1) | avg=0.260\nStep 111 | shape_mismatch            | ['0.24', '0.32'] | avg=0.282\nStep 112 | wrong_device              | ['0.26', '0.23', '0.26'] | avg=0.250\nStep 113 | shape_mismatch            | ['0.45', '0.26'] | avg=0.352\nStep 114 | training_collapse         | skipped (valid=1) | avg=0.230\nStep 115 | wrong_device              | ['0.35', '0.26'] | avg=0.305\nStep 116 | training_collapse         | skipped (valid=1) | avg=0.200\nStep 117 | shape_mismatch            | ['0.32', '0.46', '0.24', '0.26'] | avg=0.321\nStep 118 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep 119 | training_collapse         | ['0.35', '0.91', '0.30'] | avg=0.520\nStep 120 | training_collapse         | skipped (valid=1) | avg=0.200\nStep 121 | training_collapse         | skipped (valid=1) | avg=0.400\nStep 122 | training_collapse         | skipped (valid=0) | avg=0.000\nStep 123 | wrong_device              | ['0.26', '0.26'] | avg=0.260 [no variance]\nStep 124 | shape_mismatch            | ['0.40', '0.20', '0.26'] | avg=0.287\nStep 125 | gradient_not_zeroed       | ['0.20', '0.20'] | avg=0.200 [no variance]\nStep 126 | training_collapse         | ['0.40', '0.20'] | avg=0.300\nStep 127 | shape_mismatch            | skipped (valid=1) | avg=0.400\nStep 128 | training_collapse         | skipped (valid=0) | avg=0.000\nStep 129 | wrong_device              | ['0.46', '0.26'] | avg=0.360\nStep 130 | wrong_device              | skipped (valid=1) | avg=0.260\nStep 131 | training_collapse         | ['0.40', '0.20', '0.20'] | avg=0.267\nStep 132 | training_collapse         | ['0.40', '0.20'] | avg=0.300\nStep 133 | shape_mismatch            | skipped (valid=1) | avg=0.200\nStep 134 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep 135 | training_collapse         | ['0.26', '0.95', '0.95'] | avg=0.720\nStep 136 | training_collapse         | skipped (valid=1) | avg=0.320\nStep 137 | shape_mismatch            | ['0.24', '0.26', '0.46'] | avg=0.322\nStep 138 | gradient_not_zeroed       | skipped (valid=1) | avg=0.305\nStep 139 | wrong_device              | skipped (valid=1) | avg=0.260\nStep 140 | wrong_device              | skipped (valid=1) | avg=0.275\nStep 141 | shape_mismatch            | ['0.29', '0.46'] | avg=0.375\nStep 142 | training_collapse         | skipped (valid=1) | avg=0.520\nStep 143 | training_collapse         | ['0.99', '0.35', '0.89'] | avg=0.743\nStep 144 | shape_mismatch            | ['0.46', '0.28'] | avg=0.368\nStep 145 | gradient_not_zeroed       | skipped (valid=1) | avg=0.335\nStep 146 | training_collapse         | ['0.29', '0.24'] | avg=0.267\nStep 147 | shape_mismatch            | skipped (valid=1) | avg=0.320\nStep 148 | shape_mismatch            | ['0.45', '0.26', '0.23'] | avg=0.312\nStep 149 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep 150 | training_collapse         | ['0.28', '0.29'] | avg=0.282\nStep 151 | training_collapse         | skipped (valid=0) | avg=0.000\nStep 152 | gradient_not_zeroed       | skipped (valid=1) | avg=0.305\nStep 153 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep 154 | shape_mismatch            | ['0.26', '0.20'] | avg=0.230\nStep 155 | gradient_not_zeroed       | skipped (valid=1) | avg=0.290\nStep 156 | shape_mismatch            | ['0.23', '0.35'] | avg=0.290\nStep 157 | training_collapse         | ['0.28', '0.35'] | avg=0.312\nStep 158 | wrong_device              | ['0.28', '0.24'] | avg=0.260\nStep 159 | wrong_device              | ['0.20', '0.26'] | avg=0.230\nStep 160 | wrong_device              | skipped (valid=1) | avg=0.260\nStep 161 | training_collapse         | ['0.26', '0.35'] | avg=0.305\nStep 162 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep 163 | shape_mismatch            | ['0.20', '0.40', '0.35'] | avg=0.317\nStep 164 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep 165 | training_collapse         | ['0.35', '0.35'] | avg=0.350 [no variance]\nStep 166 | shape_mismatch            | ['0.20', '0.23'] | avg=0.215\nStep 167 | training_collapse         | ['0.29', '0.20'] | avg=0.245\nStep 168 | gradient_not_zeroed       | skipped (valid=1) | avg=0.200\nStep 169 | wrong_device              | ['0.20', '0.26'] | avg=0.230\nStep 170 | wrong_device              | skipped (valid=0) | avg=0.000\nStep 171 | shape_mismatch            | ['0.23', '0.26'] | avg=0.245\nStep 172 | gradient_not_zeroed       | skipped (valid=1) | avg=0.230\nStep 173 | gradient_not_zeroed       | skipped (valid=1) | avg=0.200\nStep 179 | training_collapse         | ['0.20', '0.20', '0.20'] | avg=0.200 [no variance]\nStep 180 | gradient_not_zeroed       | skipped (valid=1) | avg=0.200\nStep 181 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep 182 | gradient_not_zeroed       | skipped (valid=1) | avg=0.230\nStep 183 | gradient_not_zeroed       | skipped (valid=1) | avg=0.200\nStep 184 | shape_mismatch            | skipped (valid=1) | avg=0.260\nStep 185 | shape_mismatch            | skipped (valid=1) | avg=0.290\nStep 186 | gradient_not_zeroed       | skipped (valid=1) | avg=0.200\nStep 187 | wrong_device              | ['0.26', '0.26'] | avg=0.260 [no variance]\nStep 188 | gradient_not_zeroed       | skipped (valid=1) | avg=0.200\nStep 189 | training_collapse         | ['0.26', '0.35'] | avg=0.305\nStep 190 | wrong_device              | skipped (valid=1) | avg=0.275\nStep 191 | shape_mismatch            | skipped (valid=0) | avg=0.000\nStep 192 | gradient_not_zeroed       | skipped (valid=0) | avg=0.000\nStep 193 | training_collapse         | ['0.20', '0.20'] | avg=0.200 [no variance]\nStep 194 | wrong_device              | skipped (valid=1) | avg=0.260\nStep 195 | wrong_device              | skipped (valid=0) | avg=0.000\nStep 196 | training_collapse         | ['0.20', '0.20', '0.20'] | avg=0.200 [no variance]\nStep 197 | wrong_device              | skipped (valid=0) | avg=0.000\nStep 198 | shape_mismatch            | ['0.46', '0.29'] | avg=0.375\nStep 199 | gradient_not_zeroed       | skipped (valid=1) | avg=0.200\nDone. Steps: 200, total skipped generations: 496\nSaved 200 steps to reward_log.json\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"","metadata":{"id":"pvH_vHpwlWxc","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# CELL 7 — Plot reward curve (dual: raw + percentage)\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# If reward_log not in memory (e.g. fresh kernel), load from saved file\nif 'reward_log' not in dir():\n    import json\n    try:\n        with open('reward_log.json') as f:\n            reward_log = json.load(f)\n        print(f'Loaded {len(reward_log)} steps from reward_log.json')\n    except FileNotFoundError:\n        print('reward_log.json not found — run Cell 6 first')\n        raise\n\nsteps = [r['step'] for r in reward_log]\nrewards = [r['reward'] for r in reward_log]\n\nwindow = 8\nsmoothed = np.convolve(rewards, np.ones(window)/window, mode='valid')\n\ninitial_avg = np.mean(rewards[:8])\nfinal_avg = np.mean(rewards[-8:])\nimprovement_pct = ((final_avg - initial_avg) / initial_avg * 100) if initial_avg > 0 else 0\n\n# Trend line\nz = np.polyfit(steps, rewards, 1)\np = np.poly1d(z)\ntrend = p(steps)\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 5))\n\n# --- Plot 1: Raw reward (0-0.99) ---\nax1.plot(steps, rewards, alpha=0.3, color='steelblue', label='Raw reward')\nax1.plot(steps[window-1:], smoothed, color='steelblue', linewidth=2.5, label=f'Smoothed (window={window})')\nax1.plot(steps, trend, color='red', linestyle='--', alpha=0.8, label=f'Trend ({\"+\" if z[0]>=0 else \"\"}{z[0]*100:.3f}/step)')\nax1.axhline(y=initial_avg, color='orange', linestyle=':', alpha=0.7, label=f'Initial avg: {initial_avg:.3f}')\nax1.axhline(y=final_avg, color='green', linestyle=':', alpha=0.7, label=f'Final avg: {final_avg:.3f}')\nax1.set_xlabel('Training Step')\nax1.set_ylabel('Reward (0 - 0.99)')\nax1.set_title('ML Debug Env — GRPO Training\\nQwen2.5-1.5B-Instruct + LoRA (4bit)')\nax1.legend()\nax1.grid(True, alpha=0.3)\nax1.set_ylim(0, 1.0)\n\n# --- Plot 2: Percentage scale ---\nrewards_pct = [r * 100 for r in rewards]\nsmoothed_pct = np.convolve(rewards_pct, np.ones(window)/window, mode='valid')\ntrend_pct = trend * 100\n\nax2.plot(steps, rewards_pct, alpha=0.3, color='darkorange', label='Raw reward %')\nax2.plot(steps[window-1:], smoothed_pct, color='darkorange', linewidth=2.5, label=f'Smoothed (window={window})')\nax2.plot(steps, trend_pct, color='red', linestyle='--', alpha=0.8, label=f'Trend ({\"+\" if z[0]>=0 else \"\"}{z[0]*100:.2f}%/step)')\nax2.axhline(y=initial_avg*100, color='orange', linestyle=':', alpha=0.7, label=f'Initial: {initial_avg*100:.1f}%')\nax2.axhline(y=final_avg*100, color='green', linestyle=':', alpha=0.7, label=f'Final: {final_avg*100:.1f}%')\nax2.set_xlabel('Training Step')\nax2.set_ylabel('Reward %')\nax2.set_title(f'ML Debug Env — GRPO Training\\n+{improvement_pct:.0f}% improvement ({initial_avg*100:.1f}% → {final_avg*100:.1f}%)')\nax2.legend()\nax2.grid(True, alpha=0.3)\nax2.set_ylim(0, 100)\n\nplt.tight_layout()\nplt.savefig('reward_curve_dual.png', dpi=150, bbox_inches='tight')\nplt.show()\n\nprint(f'Initial reward (first 8 steps): {initial_avg:.3f} ({initial_avg*100:.1f}%)')\nprint(f'Final reward (last 8 steps):    {final_avg:.3f} ({final_avg*100:.1f}%)')\nprint(f'Improvement: {final_avg - initial_avg:+.3f} ({improvement_pct:+.1f}%)')","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":211},"id":"TUqbZB99nC2N","outputId":"c33a139e-3914-4d49-ffdc-b2cca9f9a24e","trusted":true,"execution":{"iopub.status.busy":"2026-04-26T05:38:17.385053Z","iopub.execute_input":"2026-04-26T05:38:17.385438Z","iopub.status.idle":"2026-04-26T05:38:18.382525Z","shell.execute_reply.started":"2026-04-26T05:38:17.385414Z","shell.execute_reply":"2026-04-26T05:38:18.381814Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1800x500 with 2 Axes>","image/png":"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reward (first 8 steps): 0.244 (24.4%)\nFinal reward (last 8 steps):    0.154 (15.4%)\nImprovement: -0.089 (-36.6%)\n","output_type":"stream"}],"execution_count":8},{"cell_type":"code","source":"# CELL 8 — Before/after test\nimport torch\nfrom unsloth import FastLanguageModel\nFastLanguageModel.for_inference(model)\n\ndef make_prompt(task_id, seed=42):\n    scenario = get_scenario(task_id, seed=seed)\n    return [\n        {\"role\": \"system\", \"content\": TRAIN_SYSTEM},\n        {\"role\": \"user\", \"content\": (\n            f\"Broken script:\\n```python\\n{scenario.buggy_code}\\n```\\n\\n\"\n            f\"Error/failure:\\n{scenario.error_output}\\n\\n\"\n            f\"Return JSON fix only.\"\n        )}\n    ], scenario\n\nTEST_TASK = 'compound_leakage_eval'\nmessages, scenario = make_prompt(TEST_TASK, seed=99)\nprompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\ninputs = tokenizer(prompt, return_tensors='pt').to('cuda')\n\nwith torch.no_grad():\n    output = model.generate(\n        **inputs,\n        max_new_tokens=600,\n        temperature=0.1,\n        do_sample=True,\n        pad_token_id=tokenizer.eos_token_id,\n    )\n\nresponse = tokenizer.decode(output[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)\nreward = compute_reward(response, TEST_TASK, seed=99)\nprint(f'Task: {TEST_TASK}')\nprint(f'Score: {reward:.3f}')\nprint(f'Response: {response[:400]}')","metadata":{"id":"7D-gg-n-qfgB","trusted":true,"execution":{"iopub.status.busy":"2026-04-26T05:38:18.383607Z","iopub.execute_input":"2026-04-26T05:38:18.383820Z","iopub.status.idle":"2026-04-26T05:38:24.414018Z","shell.execute_reply.started":"2026-04-26T05:38:18.383800Z","shell.execute_reply":"2026-04-26T05:38:24.413064Z"}},"outputs":[{"name":"stdout","text":"Task: compound_leakage_eval\nScore: 0.550\nResponse: ```json\n{\n  \"bug_type\": \"data_leakage\",\n  \"diagnosis\": \"The normalization statistics were calculated on the full dataset without considering the split, leading to data leakage.\",\n  \"fixed_code\": \"def __init__(self, input_dim, num_classes):\\n    super().__init__()\\n\\n    # Calculate statistics on the training set only\\n    mean = X_train.mean(dim=0)\\n    std = X_train.std(dim=0) + 1e-8\"\n}\n```\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"# CELL 9 — Save everything\nimport json\n\n# Save reward log\nwith open('reward_log.json', 'w') as f:\n    json.dump(reward_log, f)\n\n# Save baseline score\nbaseline = {'task': 'compound_leakage_eval', 'seed': 99, 'score': 0.0, 'response': 'inspect:print_shapes'}\nwith open('baseline_result.json', 'w') as f:\n    json.dump(baseline, f)\n\n# Download reward curve\n# On Kaggle — save to /kaggle/working/ which is automatically downloadable\nimport shutil\nshutil.copy('reward_curve_dual.png', '/kaggle/working/reward_curve_dual.png')\nprint('Saved to /kaggle/working/ — download from the Output tab on the right')\n\nprint('Saved. Download reward_curve.png from the files panel.')","metadata":{"id":"lUqWq9Ibq0HD","trusted":true,"execution":{"iopub.status.busy":"2026-04-26T05:49:35.725958Z","iopub.execute_input":"2026-04-26T05:49:35.726554Z","iopub.status.idle":"2026-04-26T05:49:35.734588Z","shell.execute_reply.started":"2026-04-26T05:49:35.726522Z","shell.execute_reply":"2026-04-26T05:49:35.733832Z"}},"outputs":[{"name":"stdout","text":"Saved to /kaggle/working/ — download from the Output tab on the right\nSaved. Download reward_curve.png from the files panel.\n","output_type":"stream"}],"execution_count":12},{"cell_type":"code","source":"","metadata":{"id":"QlpYCHYkrL7b","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":" ","metadata":{"id":"V1BMIoisrs_B","trusted":true,"execution":{"iopub.status.busy":"2026-04-26T05:38:24.449400Z","iopub.status.idle":"2026-04-26T05:38:24.449743Z","shell.execute_reply.started":"2026-04-26T05:38:24.449573Z","shell.execute_reply":"2026-04-26T05:38:24.449598Z"},"outputId":"5709a6aa-0298-48ae-a266-7f24d6140870"},"outputs":[],"execution_count":null},{"cell_type":"code","source":" ","metadata":{"id":"LAoYRCWNrs3p","trusted":true,"execution":{"iopub.status.busy":"2026-04-26T05:38:24.451443Z","iopub.status.idle":"2026-04-26T05:38:24.451862Z","shell.execute_reply.started":"2026-04-26T05:38:24.451664Z","shell.execute_reply":"2026-04-26T05:38:24.451686Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"id":"XVsq7flArsvK","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"id":"oCY3A2eirthG","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"id":"tTU-zxvZ1p8m","trusted":true},"outputs":[],"execution_count":null}]}