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   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Liquid4All/cookbook/blob/main/finetuning/notebooks/grpo_with_trl_ifstruct.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "# Fine-tune LFM2.5-350M for Structured Output Compliance with TRL GRPO\n",
    "\n",
    "In this notebook, we will walk through how to fine-tune an [LFM2.5-350M model](https://huggingface.co/LiquidAI/LFM2.5-350M) with GRPO using the [TRL library](https://huggingface.co/docs/trl/en/index).\n",
    "\n",
    "The goal of this notebook is to train **schema compliance only**, as described in the [IFStruct blog post](https://www.liquid.ai/blog/ifstruct-v1.0). The rewards do not judge whether extracted values are semantically correct. Instead, they only judge whether the generated response follows the requested JSON format and JSON Schema.\n",
    "\n",
    "This notebook covers the training pipeline discussed in the accompanying blog post [\"accompanying blog post\"](https://huggingface.co/blog/grpo-with-trl-ifstruct).\n",
    "\n",
    "**Note, this is not the training pipeline used to train the RL model described in the [IFStruct blog post](https://www.liquid.ai/blog/ifstruct-v1.0). The goal of this notebook is not to recreate the IFStruct benchmark score but to show how fine-tuning can improve eval scores.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7KfPr9jKFNiK"
   },
   "source": [
    "## Prerequisites\n",
    "\n",
    "**Hardware and runtime.** This notebook can be run on a free-tier Colab or Kaggle notebook. Expect about 4 hours for a 100-step training run on a T4 (or 1.5 hours on A100).\n",
    "\n",
    "**Hugging Face token (optional)** If you want to enable authenticated Hub downloads or push the trained model to the Hugging Face Hub, store an `HF_TOKEN` in your platform's secrets manager or the environment variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VGqVguiIFNiK"
   },
   "source": [
    "## Install required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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      "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/842.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m842.4/842.4 kB\u001b[0m \u001b[31m62.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/11.5 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m9.9/11.5 MB\u001b[0m \u001b[31m296.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m11.5/11.5 MB\u001b[0m \u001b[31m288.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.5/11.5 MB\u001b[0m \u001b[31m151.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m127.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m555.1/555.1 kB\u001b[0m \u001b[31m50.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.9/48.9 MB\u001b[0m \u001b[31m48.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "%pip install -q \"trl==1.7.1\" \"transformers==5.13.0\" \"peft==0.19.1\" \"torchao>=0.16.0\" datasets accelerate jsonschema matplotlib"
   ]
  },
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    "tags": []
   },
   "source": [
    "## Imports and Configuration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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   "source": [
    "import os\n",
    "\n",
    "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\" # Let PyTorch grow memory segments to reduce fragmentation and OOMs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:torchao:Failed to load /usr/local/lib/python3.12/dist-packages/torchao/_C_cutlass_90a.abi3.so: Could not load this library: /usr/local/lib/python3.12/dist-packages/torchao/_C_cutlass_90a.abi3.so\n",
      "WARNING:torchao:Failed to load /usr/local/lib/python3.12/dist-packages/torchao/_C_mxfp8.cpython-310-x86_64-linux-gnu.so: Could not load this library: /usr/local/lib/python3.12/dist-packages/torchao/_C_mxfp8.cpython-310-x86_64-linux-gnu.so\n"
     ]
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   ],
   "source": [
    "import torch\n",
    "import transformers\n",
    "\n",
    "# Uncomment to set HF_TOKEN in Kaggle\n",
    "# from kaggle_secrets import UserSecretsClient\n",
    "# os.environ[\"HF_TOKEN\"] = UserSecretsClient().get_secret(\"HF_TOKEN\")\n",
    "\n",
    "SEED = 42\n",
    "transformers.set_seed(SEED)  # seeds python, numpy, and torch\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    device = \"cuda\"\n",
    "elif torch.backends.mps.is_available():\n",
    "    device = \"mps\"\n",
    "else:\n",
    "    device = \"cpu\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "tags": []
   },
   "source": [
    "## Load Dataset\n",
    "\n",
    "For this tutorial, we will use the open [`nvidia/Nemotron-RL-instruction_following-structured_outputs`](https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following-structured_outputs) dataset, which tests the ability of a model to follow output JSON formatting instructions under schema constraints."
   ]
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
      "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
     ]
    },
    {
     "data": {
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       "model_id": "7d204f741c724f4a9e985b11ae83adea",
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      "text/plain": [
       "README.md:   0%|          | 0.00/2.66k [00:00<?, ?B/s]"
      ]
     },
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    },
    {
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      "text/plain": [
       "structured_outputs_251027_nano_v3_sdg_js(…):   0%|          | 0.00/91.0M [00:00<?, ?B/s]"
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     },
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       "version_minor": 0
      },
      "text/plain": [
       "(…)utputs_251027_nano_v3_sdg_json_val.jsonl:   0%|          | 0.00/4.92M [00:00<?, ?B/s]"
      ]
     },
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    },
    {
     "data": {
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       "model_id": "c573e045591f476a9845b67b056f1c85",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/9437 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/512 [00:00<?, ? examples/s]"
      ]
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    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['responses_create_params', 'schema_str', 'schema_type', 'schema_fields_count'],\n",
       "    num_rows: 1000\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "DATASET_ID = \"nvidia/Nemotron-RL-instruction_following-structured_outputs\"\n",
    "TRAIN_SAMPLES = 1000  # filtered down to about 500 during data cleaning\n",
    "\n",
    "train_ds = load_dataset(DATASET_ID, split=f\"train[:{TRAIN_SAMPLES}]\")\n",
    "\n",
    "train_ds"
   ]
  },
  {
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   "source": [
    "## Data Preparation\n",
    "\n",
    "[`GRPOTrainer`](https://huggingface.co/docs/trl/en/grpo_trainer) expects a `prompt` column in the format `{\"role\":  ..., \"content\":...}`. The `responses_create_params` column already contains this information under the `\"input\"` field. That's why we will replace the column `responses_create_params` with `row[\"responses_create_params\"][\"input\"]` and rename it to `prompt`.\n",
    "\n",
    "Reward functions receive the other columns (`schema_str`, `schema_type`, and `schema_fields_count`) through `**kwargs`.\n",
    "\n",
    "We will apply the following **data cleaning** steps:\n",
    "\n",
    "- `prompt_fits`: Prompts longer than 6,000 characters (~1,600 tokens) are dropped to reduce step time on free-tier GPUs.\n",
    "- `schema_is_valid`: Rows with unusable JSON schema (e.g., not parseable or not valid) are dropped as well.\n",
    "\n",
    "We will also apply a **data augmentation step**, `augment`, to close two gaps between the training data and the IFStruct benchmark: Nemotron prompts ask for a single top-level JSON object as raw JSON, so a model trained on them never practices fenced output or bare top-level arrays. `augment` therefore rewrites a percentage of the training prompts:\n",
    "\n",
    "- 40% get `\"Return the output inside a fenced code block.\"` appended, so the model learns to follow the format instruction rather than a fixed habit.\n",
    "- A disjoint 20% become top-level-array tasks: the schema is wrapped in `{\"type\": \"array\", \"items\": ..., \"minItems\": n, \"maxItems\": n}` and the prompt asks for exactly n such objects, which also trains item-count compliance.\n",
    "\n",
    "*Note: the IFStruct benchmark also contains 50% of YAML samples. For convenience, we will only focus on JSON in this article.*"
   ]
  },
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01009aeff2124481bee115448d677b25",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
      ]
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       "Filter:   0%|          | 0/1000 [00:00<?, ? examples/s]"
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    {
     "name": "stdout",
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     "text": [
      "573 train samples left after prompt length filtering\n"
     ]
    },
    {
     "data": {
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       "Filter:   0%|          | 0/573 [00:00<?, ? examples/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537 train samples left after valid schema filtering\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.12/dist-packages/jsonschema/_format.py:419: FutureWarning: Possible nested set at position 5\n",
      "  return bool(re.compile(instance))\n"
     ]
    },
    {
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       "Map:   0%|          | 0/537 [00:00<?, ? examples/s]"
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     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['schema_str', 'schema_type', 'schema_fields_count', 'prompt', 'wants_fence'],\n",
       "    num_rows: 537\n",
       "})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "from typing import Any\n",
    "from jsonschema import Draft7Validator\n",
    "\n",
    "# Create prompt field\n",
    "def adapt_row(row: dict[str, Any]) -> dict[str, Any]:\n",
    "    return {\n",
    "        \"prompt\": row[\"responses_create_params\"][\"input\"],\n",
    "        \"wants_fence\": False,  # ground-truth format flag; flipped to True by the augment step\n",
    "    }\n",
    "\n",
    "train_ds = train_ds.map(adapt_row, remove_columns=[\"responses_create_params\"])\n",
    "\n",
    "# Remove long samples\n",
    "def prompt_fits(row: dict[str, Any]) -> bool:\n",
    "    MAX_PROMPT_CHARS = 6000\n",
    "    return sum(len(message[\"content\"]) for message in row[\"prompt\"]) <= MAX_PROMPT_CHARS\n",
    "\n",
    "train_ds = train_ds.filter(prompt_fits)\n",
    "print(f\"{len(train_ds)} train samples left after prompt length filtering\")\n",
    "\n",
    "# Remove data with invalid schema\n",
    "def schema_is_valid(row: dict[str, Any]) -> bool:\n",
    "    if '\"$ref\"' in row[\"schema_str\"]:\n",
    "        return False\n",
    "    try:\n",
    "        Draft7Validator.check_schema(json.loads(row[\"schema_str\"]))\n",
    "    except Exception:\n",
    "        return False\n",
    "    return True\n",
    "\n",
    "train_ds = train_ds.filter(schema_is_valid)\n",
    "print(f\"{len(train_ds)} train samples left after valid schema filtering\")\n",
    "\n",
    "# Deterministic (index-based, no RNG) prompt augmentation, one class per row:\n",
    "#   idx % 5 in {0, 1} -> append a fenced-output instruction        (40%)\n",
    "#   idx % 5 == 2      -> convert into a top-level-array task       (20%)\n",
    "#   idx % 5 in {3, 4} -> unchanged                                 (40%)\n",
    "def augment(row: dict[str, Any], idx: int) -> dict[str, Any]:\n",
    "    variant = idx % 5\n",
    "    if variant > 2:\n",
    "        return row\n",
    "    messages = [dict(message) for message in row[\"prompt\"]]\n",
    "    if variant == 2:\n",
    "        n = 2 + idx % 3  # 2-4 items, varied so the count must be read from the prompt\n",
    "        array_schema = {\"type\": \"array\", \"items\": json.loads(row[\"schema_str\"]), \"minItems\": n, \"maxItems\": n}\n",
    "        messages[-1][\"content\"] += f\"\\n\\nReturn a JSON array containing exactly {n} of these objects.\"\n",
    "        return {**row, \"prompt\": messages, \"schema_str\": json.dumps(array_schema)}\n",
    "    messages[-1][\"content\"] += \"\\n\\nReturn the output inside a fenced code block.\"\n",
    "    return {**row, \"prompt\": messages, \"wants_fence\": True}\n",
    "\n",
    "train_ds = train_ds.map(augment, with_indices=True)\n",
    "\n",
    "train_ds"
   ]
  },
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   "source": [
    "## Loading the Baseline Model\n",
    "\n",
    "We'll load [`\"LiquidAI/LFM2.5-350M\"`](https://huggingface.co/LiquidAI/LFM2.5-350M) as the base model and tokenizer."
   ]
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    {
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       "config.json:   0%|          | 0.00/1.28k [00:00<?, ?B/s]"
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       "model.safetensors:   0%|          | 0.00/709M [00:00<?, ?B/s]"
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    {
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      "text/plain": [
       "Loading weights:   0%|          | 0/148 [00:00<?, ?it/s]"
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     },
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   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "MODEL_ID = \"LiquidAI/LFM2.5-350M\"\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    MODEL_ID,\n",
    "    trust_remote_code=True,\n",
    "    dtype=torch.bfloat16 if device == \"mps\" or torch.cuda.is_bf16_supported() else torch.float16,\n",
    "    device_map=\"auto\",\n",
    ")\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)"
   ]
  },
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   "source": [
    "## Configuring LoRA\n",
    "\n",
    "Next, we'll configure LoRA for training, which allows us to efficiently fine-tune the model.\n",
    "LFM models use a hybrid architecture, so we'll use explicit LFM module names, including attention projections, hybrid/conv projection, and feed-forward layers."
   ]
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     "start_time": "2026-07-06T19:59:31.604681+00:00",
     "status": "completed"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 5,996,544 || all params: 360,480,512 || trainable%: 1.6635\n"
     ]
    }
   ],
   "source": [
    "from peft import LoraConfig, get_peft_model\n",
    "\n",
    "lora_config = LoraConfig(\n",
    "    r=16,\n",
    "    lora_alpha=32,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    "    target_modules=[\n",
    "        \"q_proj\", \"k_proj\", \"v_proj\", \n",
    "        \"out_proj\", \"in_proj\",\n",
    "        \"w1\", \"w2\", \"w3\",\n",
    "    ],\n",
    ")\n",
    "\n",
    "model = get_peft_model(model, lora_config)\n",
    "\n",
    "model.print_trainable_parameters()"
   ]
  },
  {
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   "metadata": {
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   "source": [
    "## Defining the Reward Functions\n",
    "\n",
    "The trainer uses three reward functions: \n",
    "- **form**: is the output parseable JSON, fenced or raw as requested?\n",
    "- **structure proxy**: top-level field count  \n",
    "- **schema compliance**: the actual training objective\n",
    "\n",
    "All rewards live in `[0, 1]`, so the `reward_weights` in the GRPO configuration read directly as relative importance.\n",
    "\n",
    "We define a helper function to extract JSON first."
   ]
  },
  {
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   "metadata": {
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   "source": [
    "import re\n",
    "\n",
    "def extract_json(text: str):\n",
    "    \"\"\"Parse a completion as raw JSON or as the first valid fenced JSON block.\n",
    "    Returns (obj, form) with form in {\"direct\", \"fenced\", None}.\"\"\"\n",
    "    if text.startswith((\"{\", \"[\")) and text.endswith((\"}\", \"]\")):\n",
    "        try:\n",
    "            return json.loads(text), \"direct\"\n",
    "        except json.JSONDecodeError:\n",
    "            pass\n",
    "    # Fences must sit on their own lines -- strict parsers reject a glued closing fence.\n",
    "    for match in re.finditer(r\"```(?:json)?\\s*\\n(.*?)\\n\\s*```\", text, re.DOTALL):\n",
    "        try:\n",
    "            return json.loads(match.group(1).strip()), \"fenced\"\n",
    "        except json.JSONDecodeError:\n",
    "            continue\n",
    "    return None, None"
   ]
  },
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   "metadata": {
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   "source": [
    "### JSON Format Reward\n",
    "\n",
    "This reward scores whether the completion parses as JSON in the form the prompt asked for:\n",
    "- `1.0` for the requested form\n",
    "- `0.2` for parseable JSON in the other form\n",
    "- `0.0` for anything unparseable. \n",
    "\n",
    "The wide `1.0` vs `0.2` gap reinforces the first sampled completion that lands in the requested form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
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   "source": [
    "def json_format_reward(completions, **kwargs) -> list[float]:\n",
    "    rewards = []\n",
    "    for completion, fence_requested in zip(completions, kwargs[\"wants_fence\"]):\n",
    "        _, form = extract_json(completion[-1][\"content\"].strip())\n",
    "\n",
    "        # Full credit only for the requested form; 0.2 keeps the wrong form above unparseable output.\n",
    "        requested = \"fenced\" if fence_requested else \"direct\"\n",
    "        rewards.append(0.0 if form is None else 1.0 if form == requested else 0.2)\n",
    "    return rewards"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "status": "completed"
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   "source": [
    "### Field Count Reward\n",
    "\n",
    "This reward compares the number of top-level fields against the row's `schema_fields_count`: full credit for an exact match, decaying linearly with the relative miss."
   ]
  },
  {
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   "execution_count": 13,
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   "source": [
    "def field_count_reward(completions, **kwargs) -> list[float]:\n",
    "    rewards = []\n",
    "    for completion, expected_count in zip(completions, kwargs[\"schema_fields_count\"]):\n",
    "        expected_count = int(expected_count)  # the dataset stores this count as a string\n",
    "        obj, _ = extract_json(completion[-1][\"content\"].strip())\n",
    "\n",
    "        # Arrays score the mean field count per object, so schema_fields_count keeps its per-object meaning.\n",
    "        if isinstance(obj, list):\n",
    "            dict_items = [item for item in obj if isinstance(item, dict)]\n",
    "            actual_count = sum(len(item) for item in dict_items) / len(dict_items) if dict_items else None\n",
    "        elif isinstance(obj, dict):\n",
    "            actual_count = len(obj)\n",
    "        else:\n",
    "            actual_count = None\n",
    "\n",
    "        if actual_count is None:\n",
    "            rewards.append(0.0)\n",
    "        elif expected_count == 0:\n",
    "            rewards.append(1.0 if actual_count == 0 else 0.0)\n",
    "        else:\n",
    "            # Exact match earns 1.0; decays linearly with the relative miss.\n",
    "            rewards.append(max(0.0, 1.0 - abs(actual_count - expected_count) / expected_count))\n",
    "    return rewards"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Schema Validation Reward\n",
    "\n",
    "This reward validates the generated object against the row's the actual JSON Schema using `jsonschema`'s `Draft7Validator`. \n",
    "\n",
    "Compliance is graded per violation: the score decays with the number of `iter_errors` violations, normalized by the schema's declared checks — counted recursively, so nested violations count like top-level ones. Per-violation credit keeps the gradient alive once completions have the right keys but aren't yet valid.\n",
    "\n",
    "Two additions shape the score:\n",
    "\n",
    "- **Required-key coverage gates it** — otherwise `{}` scores ~0.5. For arrays: wrong shape scores zero, required keys are checked *per item*, under-length loses coverage proportionally.\n",
    "- **Zero violations earn the top 25%** (`0.75 × graded + 0.25 × fully-valid`), keeping the reward in `[0, 1]`."
   ]
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   "source": [
    "def count_schema_checks(schema) -> int:\n",
    "    \"\"\"Recursively count declared constraints so nested violations weigh as much as top-level ones.\"\"\"\n",
    "\n",
    "    constraint_keywords = (\n",
    "        \"enum\", \"pattern\", \"minItems\", \"maxItems\", \"minimum\", \"maximum\",\n",
    "        \"minLength\", \"maxLength\", \"additionalProperties\",\n",
    "    )\n",
    "\n",
    "    if isinstance(schema, list):\n",
    "        return sum(count_schema_checks(item) for item in schema)\n",
    "    if not isinstance(schema, dict):\n",
    "        return 0\n",
    "    # Data-valued keywords (examples, default, enum, const) may hold dicts that\n",
    "    # merely look like schemas — e.g. {\"required\": true} — so type-check before\n",
    "    # taking len() instead of trusting the meta-schema for these values.\n",
    "    properties = schema.get(\"properties\")\n",
    "    required = schema.get(\"required\")\n",
    "    count = len(properties) if isinstance(properties, dict) else 0\n",
    "    count += len(required) if isinstance(required, list) else 0\n",
    "    count += sum(1 for keyword in constraint_keywords if keyword in schema)\n",
    "    count += sum(count_schema_checks(value) for value in schema.values() if isinstance(value, (dict, list)))\n",
    "    return count\n",
    "\n",
    "def schema_validation_reward(completions, **kwargs) -> list[float]:\n",
    "    rewards = []\n",
    "    for completion, schema_text in zip(completions, kwargs[\"schema_str\"]):\n",
    "        schema = json.loads(schema_text)\n",
    "        obj, _ = extract_json(completion[-1][\"content\"].strip())\n",
    "\n",
    "        # Model output is untrusted: anything that isn't an object or array scores 0.\n",
    "        if not isinstance(obj, (dict, list)):\n",
    "            rewards.append(0.0)\n",
    "            continue\n",
    "\n",
    "        # Coverage gates partial credit so degenerate output ({}, [], or the wrong\n",
    "        # top-level shape) scores ~0 instead of harvesting a permissive denominator.\n",
    "        if (schema.get(\"type\") == \"array\") != isinstance(obj, list):\n",
    "            coverage = 0.0\n",
    "        elif isinstance(obj, dict):\n",
    "            required = schema.get(\"required\", [])\n",
    "            coverage = sum(key in obj for key in required) / len(required) if required else 1.0\n",
    "        elif not obj:\n",
    "            coverage = 0.0\n",
    "        else:\n",
    "            # Under-length arrays lose credit proportionally -- otherwise fewer\n",
    "            # imperfect items would out-score the requested count.\n",
    "            n_min = schema.get(\"minItems\")\n",
    "            coverage = min(len(obj), n_min) / n_min if n_min else 1.0\n",
    "            items = schema.get(\"items\")\n",
    "            required = items.get(\"required\", []) if isinstance(items, dict) else []\n",
    "            if required:\n",
    "                coverage *= sum(\n",
    "                    sum(key in item for key in required) for item in obj if isinstance(item, dict)\n",
    "                ) / (len(required) * len(obj))\n",
    "\n",
    "        errors = list(Draft7Validator(schema).iter_errors(obj))\n",
    "\n",
    "        # Normalize by every declared check -- per expected item for arrays, so a\n",
    "        # full-length answer isn't penalized harder for the same per-item flaw.\n",
    "        num_checks = count_schema_checks(schema)\n",
    "        if isinstance(obj, list) and schema.get(\"minItems\"):\n",
    "            num_checks += (schema[\"minItems\"] - 1) * count_schema_checks(schema.get(\"items\"))\n",
    "\n",
    "        partial = coverage * max(0.0, 1.0 - len(errors) / max(1, num_checks))\n",
    "        # The top 25% of the range is reserved for fully valid output.\n",
    "        rewards.append(0.75 * partial + (0.25 if not errors else 0.0))\n",
    "    return rewards"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "status": "completed"
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   },
   "source": [
    "Let's check that the reward functions can score completions against an actual schema before training starts."
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
    "colab": {
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    "tags": [],
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "direct_object\n",
      "json_format (wants_fence=False) [1.0]\n",
      "json_format (wants_fence=True)  [0.2]\n",
      "field_count [0.0]\n",
      "schema_validation [0.0]\n",
      "fenced\n",
      "json_format (wants_fence=False) [0.2]\n",
      "json_format (wants_fence=True)  [1.0]\n",
      "field_count [0.16666666666666663]\n",
      "schema_validation [0.0]\n",
      "backtick_spam\n",
      "json_format (wants_fence=False) [0.0]\n",
      "json_format (wants_fence=True)  [0.0]\n",
      "field_count [0.0]\n",
      "schema_validation [0.0]\n"
     ]
    }
   ],
   "source": [
    "def as_chat(text: str) -> list[dict]:\n",
    "    # The completion shape GRPOTrainer passes to reward functions.\n",
    "    return [{\"role\": \"assistant\", \"content\": text}]\n",
    "\n",
    "direct_object = \"{}\"\n",
    "fenced = \"Here is the JSON: ```json\\n{\\\"unexpected\\\": true}\\n```\"\n",
    "backtick_spam = \"`\" * 2048  # the degenerate output a collapsed model produces under greedy decoding\n",
    "\n",
    "for label, completion in [(\"direct_object\", direct_object), (\"fenced\", fenced), (\"backtick_spam\", backtick_spam)]:\n",
    "    print(completion)\n",
    "    print(\"json_format (wants_fence=False)\", json_format_reward([as_chat(completion)], wants_fence=[False]))\n",
    "    print(\"json_format (wants_fence=True) \", json_format_reward([as_chat(completion)], wants_fence=[True]))\n",
    "    print(\"field_count\", field_count_reward([as_chat(completion)], schema_fields_count=[train_ds[0][\"schema_fields_count\"]]))\n",
    "    print(\"schema_validation\", schema_validation_reward([as_chat(completion)], schema_str=[train_ds[0][\"schema_str\"]]))"
   ]
  },
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   "source": [
    "We'll pass the three reward functions to `GRPOTrainer` as a list. During training, TRL calls each function on every generation group and combines the results as a **weighted sum**. The weights are set explicitly with `reward_weights` in the GRPO configuration below, which is why all three functions were kept on the same `[0, 1]` scale."
   ]
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   "source": [
    "reward_funcs = [\n",
    "    json_format_reward,\n",
    "    field_count_reward,\n",
    "    schema_validation_reward,\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5c016c23",
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     "duration": 0.032513,
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     "start_time": "2026-07-06T20:01:48.364403+00:00",
     "status": "completed"
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   },
   "source": [
    "## Configuring GRPO Training Parameters\n",
    "\n",
    "These settings are sized for a free-tier GPU (Colab/Kaggle T4) and a 500-sample training pool. \n",
    "\n",
    "Some notable hyperparameter setting are:\n",
    "- `reward_weights=[1.0, 0.5, 2.0]` weights the summed rewards.\n",
    "- `temperature=1.1` samples slightly hotter than the TRL default (1.0) to keep the 8 completions in a group varied.\n",
    "- `beta=0.01` sets the KL penalty that anchors the policy to the frozen reference model. A small non-zero value lets the adapter move while keeping generations close enough to the base model to stay coherent.\n",
    "\n",
    "Each optimizer step averages 4 prompt groups (`per_device_train_batch_size=4` × `gradient_accumulation_steps=8` = 32 completions = 4 groups of `num_generations=8`). \n",
    "`steps_per_generation=2` keeps each generation round to 8 sequences (one prompt group). "
   ]
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    {
     "data": {
      "text/plain": [
       "GRPOConfig(output_dir='./outputs/lfm25-350m-nemotron-schema-grpo', per_device_train_batch_size=4, num_train_epochs=3.0, max_steps=100, learning_rate=5e-05, lr_scheduler_type=<SchedulerType.LINEAR: 'linear'>, lr_scheduler_kwargs=None, warmup_steps=10, optim=<OptimizerNames.ADAMW_TORCH_FUSED: 'adamw_torch_fused'>, optim_args=None, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, optim_target_modules=None, gradient_accumulation_steps=8, average_tokens_across_devices=True, max_grad_norm=1.0, label_smoothing_factor=0.0, bf16=True, fp16=False, bf16_full_eval=False, fp16_full_eval=False, tf32=None, gradient_checkpointing=True, gradient_checkpointing_kwargs=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, use_liger_kernel=False, liger_kernel_config=None, use_cache=False, neftune_noise_alpha=None, torch_empty_cache_steps=None, auto_find_batch_size=False, logging_strategy=<IntervalStrategy.STEPS: 'steps'>, logging_steps=1, logging_first_step=False, log_on_each_node=True, logging_nan_inf_filter=True, include_num_input_tokens_seen='no', log_level='passive', log_level_replica='warning', disable_tqdm=False, report_to=[], run_name=None, project='huggingface', trackio_space_id=None, trackio_bucket_id=None, trackio_static_space_id=None, eval_strategy=<IntervalStrategy.NO: 'no'>, eval_steps=None, eval_delay=0, per_device_eval_batch_size=8, prediction_loss_only=False, eval_on_start=False, eval_do_concat_batches=True, eval_use_gather_object=False, eval_accumulation_steps=None, include_for_metrics=[], batch_eval_metrics=False, save_only_model=False, save_strategy=<SaveStrategy.STEPS: 'steps'>, save_steps=100, save_on_each_node=False, save_total_limit=None, enable_jit_checkpoint=False, push_to_hub=False, hub_token=None, hub_private_repo=None, hub_model_id=None, hub_strategy=<HubStrategy.EVERY_SAVE: 'every_save'>, hub_always_push=False, hub_revision=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, ignore_data_skip=False, restore_callback_states_from_checkpoint=False, full_determinism=False, seed=42, data_seed=None, use_cpu=False, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=None, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False), parallelism_config=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, dataloader_persistent_workers=False, dataloader_prefetch_factor=None, remove_unused_columns=False, label_names=None, train_sampling_strategy='random', length_column_name='length', ddp_find_unused_parameters=None, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=None, ddp_static_graph=None, ddp_backend=None, ddp_timeout=1800, fsdp=None, fsdp_config=None, deepspeed=None, debug=[], skip_memory_metrics=True, do_train=False, do_eval=False, do_predict=False, resume_from_checkpoint=None, warmup_ratio=None, logging_dir=None, local_rank=-1, model_init_kwargs=None, trust_remote_code=False, router_aux_loss_coef=0.001, disable_dropout=False, cast_lm_head_to_fp32=False, num_generations=8, num_generations_eval=None, max_completion_length=1024, ds3_gather_for_generation=True, shuffle_dataset=True, pad_to_multiple_of=None, generation_batch_size=8, steps_per_generation=2, temperature=1.1, top_p=1.0, top_k=0, min_p=None, generation_kwargs=None, chat_template_kwargs=None, repetition_penalty=1.0, cache_implementation=None, use_vllm=False, vllm_mode='colocate', vllm_model_impl='vllm', vllm_enable_sleep_mode=False, vllm_structured_outputs_regex=None, vllm_server_base_url=None, vllm_server_host='0.0.0.0', vllm_server_port=8000, vllm_server_timeout=240.0, vllm_group_port=51216, vllm_gpu_memory_utilization=0.3, vllm_max_model_length=None, vllm_tensor_parallel_size=1, beta=0.01, num_iterations=1, epsilon=0.2, delta=None, epsilon_high=None, sapo_temperature_neg=1.05, sapo_temperature_pos=1.0, vespo_k_pos=2.0, vespo_lambda_pos=3.0, vespo_k_neg=3.0, vespo_lambda_neg=2.0, importance_sampling_level='token', reward_weights=[1.0, 0.5, 2.0], multi_objective_aggregation='sum_then_normalize', scale_rewards='group', loss_type='dapo', mask_truncated_completions=False, sync_ref_model=False, ref_model_mixup_alpha=0.6, ref_model_sync_steps=512, top_entropy_quantile=1.0, max_tool_calling_iterations=None, vllm_importance_sampling_correction=True, vllm_importance_sampling_mode='sequence_mask', vllm_importance_sampling_clip_max=3.0, vllm_importance_sampling_clip_min=None, off_policy_mask_threshold=None, use_bias_correction_kl=False, log_completions=False, num_completions_to_print=None, log_unique_prompts=False, log_completions_hub_repo=None, use_transformers_continuous_batching=False, transformers_continuous_batching_config=None, use_transformers_paged=False, vllm_importance_sampling_cap=None)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from trl import GRPOConfig\n",
    "\n",
    "OUTPUT_DIR = \"./outputs/lfm25-350m-nemotron-schema-grpo\"\n",
    "MAX_STEPS = 100\n",
    "NUM_GENERATIONS = 8\n",
    "\n",
    "training_args = GRPOConfig(\n",
    "    output_dir=OUTPUT_DIR,\n",
    "    learning_rate=5e-5,\n",
    "    warmup_steps=max(1, int(0.1 * MAX_STEPS)), # 10% of steps used for warmup\n",
    "    max_steps=MAX_STEPS,\n",
    "\n",
    "    bf16=torch.cuda.is_available() and torch.cuda.is_bf16_supported(),\n",
    "    fp16=torch.cuda.is_available() and not torch.cuda.is_bf16_supported(),\n",
    "\n",
    "    # Rollout generation and reward settings\n",
    "    max_completion_length=1024,  # Room for nested JSON; a tight cap zeroes out whole groups.\n",
    "    mask_truncated_completions=False,  # Keep truncated completions in the loss so run-on output is penalized.\n",
    "    num_generations=NUM_GENERATIONS,\n",
    "    temperature=1.1,  # Hotter sampling keeps groups varied; identical rewards mean zero gradient.\n",
    "    reward_weights=[1.0, 0.5, 2.0],\n",
    "    per_device_train_batch_size=4,\n",
    "    gradient_accumulation_steps=8,  # Four prompt groups per optimizer update tame noisy group-level advantages.\n",
    "    steps_per_generation=2,  # Generate 8 completions at a time to fit on free T4\n",
    "    beta=0.01,  # KL penalty anchoring the policy to the reference model.\n",
    "\n",
    "    logging_steps=1,\n",
    "    save_steps=MAX_STEPS,\n",
    "    report_to=\"none\",  # Alternatively, \"wandb\" or \"tensorboard\"\n",
    ")\n",
    "\n",
    "training_args"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4ce70ad8",
    "papermill": {
     "duration": 0.033787,
     "end_time": "2026-07-06T20:01:48.602061+00:00",
     "exception": false,
     "start_time": "2026-07-06T20:01:48.568274+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training the Model\n",
    "\n",
    "Finally, we'll create the `GRPOTrainer`, pass the reward functions, and train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "execution": {
     "iopub.execute_input": "2026-07-08T10:37:02.533516Z",
     "iopub.status.busy": "2026-07-08T10:37:02.532648Z",
     "iopub.status.idle": "2026-07-08T10:52:11.852435Z",
     "shell.execute_reply": "2026-07-08T10:52:11.851780Z",
     "shell.execute_reply.started": "2026-07-08T10:37:02.533480Z"
    },
    "id": "b8122171",
    "outputId": "9520af7f-03a8-4ccd-8421-c84e1b04f081",
    "papermill": {
     "duration": 14843.315987,
     "end_time": "2026-07-07T00:09:11.950481+00:00",
     "exception": false,
     "start_time": "2026-07-06T20:01:48.634494+00:00",
     "status": "completed"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [100/100 1:33:26, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.025520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.604942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.341292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.058715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.422657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>-0.375062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>-0.297173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.241649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.013751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>-0.248826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.035345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.012719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.210780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>-0.125959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.048604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>-0.363126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.265753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.074130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.665691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.147309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.201630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.262925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>-0.201827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>-0.169264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>-0.385295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>-0.048582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.070433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>-0.014576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>-0.183160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.103950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.033979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>-0.145577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>-0.343341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>-0.310482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.019105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>-0.098093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>-0.067153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>-0.431492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>-0.142317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>-0.027284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>-0.158428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>-0.131286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>-0.022133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.027732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.214246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>-0.124955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>-0.002332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.010830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.059631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>-0.010323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>-0.092458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.009974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>-0.035169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.105801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>-0.409308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.001772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>-0.039107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.366815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>-0.072027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>-0.223267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>-0.118638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.075165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.093566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>-0.190264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.157312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>-0.221760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.049522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.032917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>-0.230302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>-0.183602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>-0.008804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>-0.199294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>-0.426130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.933532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.086201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.008001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.004614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>-0.125298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.033233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.041392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>-0.085532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>-0.003431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.002942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>-0.073464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.003901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>-0.111867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>-0.037563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.035063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>-0.059388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.144980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>-0.013638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>-0.143463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>-0.372621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.094632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>-0.133551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.233157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>-0.548110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>-0.499004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>-0.330651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.004724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.12/dist-packages/jsonschema/_keywords.py:218: FutureWarning: Possible nested set at position 5\n",
      "  and not re.search(patrn, instance)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=100, training_loss=-0.007232263050973416, metrics={'train_runtime': 5683.784, 'train_samples_per_second': 0.563, 'train_steps_per_second': 0.018, 'total_flos': 0.0, 'train_loss': -0.007232263050973416, 'epoch': 0.74487895716946})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from trl import GRPOTrainer\n",
    "\n",
    "trainer = GRPOTrainer(\n",
    "    model=model,\n",
    "    reward_funcs=reward_funcs,\n",
    "    args=training_args,\n",
    "    train_dataset=train_ds,\n",
    ")\n",
    "\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bEiyJs38FNiN",
    "papermill": {
     "duration": 0.036304,
     "end_time": "2026-07-07T00:09:12.023276+00:00",
     "exception": false,
     "start_time": "2026-07-07T00:09:11.986972+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Below you can see some logs from the training run.\n",
    "\n",
    "- **Reward components** should drift upward \n",
    "- **Combined reward** should drift upward. The band is the learning signal: GRPO's advantages come from within-group reward differences, so a band that collapses to zero means the groups have gone dead.\n",
    "- **KL from the reference model**: Flat at 0 means the policy is not moving (learning rate too low); a jump of several orders of magnitude means it is moving too fast.\n",
    "- **Group health**: a dead-group fraction around 0.25 early is normal, and it often *rises* late as rewards saturate. The truncated-completion fraction should stay around zero. A sustained climb means completions are hitting `max_completion_length`, the classic early sign of degenerate run-on output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 665
    },
    "execution": {
     "iopub.execute_input": "2026-07-08T11:16:56.570703Z",
     "iopub.status.busy": "2026-07-08T11:16:56.569760Z",
     "iopub.status.idle": "2026-07-08T11:16:57.391366Z",
     "shell.execute_reply": "2026-07-08T11:16:57.390534Z",
     "shell.execute_reply.started": "2026-07-08T11:16:56.570669Z"
    },
    "id": "06a30ee6",
    "outputId": "7ea473ef-6737-45ba-aeea-e54e9815200d",
    "papermill": {
     "duration": 0.748589,
     "end_time": "2026-07-07T00:09:12.807205+00:00",
     "exception": false,
     "start_time": "2026-07-07T00:09:12.058616+00:00",
     "status": "completed"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "logs = pd.DataFrame(trainer.state.log_history)\n",
    "logs = logs[logs[\"reward\"].notna()].set_index(\"step\")\n",
    "ROLL = 10  # each step covers only 4 prompts; judge trends on a rolling mean\n",
    "\n",
    "\n",
    "def raw_and_rolling(ax, series, label=None):\n",
    "    \"\"\"Plot the noisy per-step values faintly and their rolling mean on top.\"\"\"\n",
    "    (faint,) = ax.plot(series.index, series, alpha=0.25)\n",
    "    ax.plot(series.index, series.rolling(ROLL, min_periods=1).mean(),\n",
    "            color=faint.get_color(), label=label)\n",
    "    return faint.get_color()\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(13, 8), sharex=True)\n",
    "\n",
    "# Reward components — watch json_format for the first move off the wrong-form value\n",
    "ax = axes[0, 0]\n",
    "for col in logs.columns:\n",
    "    if col.startswith(\"rewards/\") and col.endswith(\"/mean\"):\n",
    "        raw_and_rolling(ax, logs[col], label=col.removeprefix(\"rewards/\").removesuffix(\"/mean\"))\n",
    "ax.set_title(\"Reward components\")\n",
    "ax.set_ylabel(\"mean reward\")\n",
    "ax.legend(fontsize=8)\n",
    "\n",
    "# Combined reward with its within-group spread — the spread is the learning signal\n",
    "ax = axes[0, 1]\n",
    "color = raw_and_rolling(ax, logs[\"reward\"], label=\"combined (weighted)\")\n",
    "if \"reward_std\" in logs:\n",
    "    rolling_mean = logs[\"reward\"].rolling(ROLL, min_periods=1).mean()\n",
    "    rolling_std = logs[\"reward_std\"].rolling(ROLL, min_periods=1).mean()\n",
    "    ax.fill_between(logs.index, rolling_mean - rolling_std, rolling_mean + rolling_std,\n",
    "                    alpha=0.15, color=color, label=\"±1 group std\")\n",
    "ax.set_title(\"Combined reward\")\n",
    "ax.legend(fontsize=8)\n",
    "\n",
    "# KL from the reference model — should lift off zero after warmup\n",
    "ax = axes[1, 0]\n",
    "if \"kl\" in logs:\n",
    "    raw_and_rolling(ax, logs[\"kl\"])\n",
    "ax.set_title(\"KL from reference model\")\n",
    "ax.set_ylabel(\"kl\")\n",
    "ax.set_xlabel(\"step\")\n",
    "\n",
    "# Group health — dead groups and truncated completions\n",
    "ax = axes[1, 1]\n",
    "if \"frac_reward_zero_std\" in logs:\n",
    "    raw_and_rolling(ax, logs[\"frac_reward_zero_std\"], label=\"dead groups (zero reward std)\")\n",
    "if \"completions/clipped_ratio\" in logs:\n",
    "    raw_and_rolling(ax, logs[\"completions/clipped_ratio\"], label=\"truncated completions\")\n",
    "ax.set_title(\"Group health\")\n",
    "ax.set_ylim(-0.05, 1.05)\n",
    "ax.set_xlabel(\"step\")\n",
    "ax.legend(fontsize=8)\n",
    "\n",
    "fig.suptitle(\"GRPO Training Metrics\")\n",
    "fig.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l1W-s8lYFNiO"
   },
   "source": [
    "## Merge the LoRA Adapter into the Base Model and Save\n",
    "\n",
    "Finally, merge the LoRA adapter back into the base model and save."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66,
     "referenced_widgets": [
      "96fb6ac8f2fe428ba01374fb71cf0b81",
      "491ca761dbf44fa19998f56c0d18ddf7",
      "31b0f73d87504a1fa8296d38c27145cd",
      "56a24fe656944b01b5f8cdd49041e042",
      "89d2dba0583b4dcfabf33ea6e5d05931",
      "3f355b333ebb4e0186aadb952c3b03f3",
      "9978c2905990470aa7fb369c0c15df9e",
      "c085f19b17a64f6294701beb4c993062",
      "3c043d8981654bf19a898cc8c35e6605",
      "14cc2c9c71494ac99a21dd05d8612bc1",
      "2579d569eeaf45d68e71137f44ed1cdf"
     ]
    },
    "id": "PhtdB2LuFNiO",
    "outputId": "d9a12c25-b5ae-418e-d273-f3b59ba7369c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96fb6ac8f2fe428ba01374fb71cf0b81",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merged model saved to ./outputs/lfm25-350m-nemotron-schema-grpo-merged\n"
     ]
    }
   ],
   "source": [
    "MERGED_DIR = f\"{training_args.output_dir}-merged\"\n",
    "\n",
    "# Optional: save just the LoRA adapter if you want to keep the standalone deltas\n",
    "# trainer.save_model(training_args.output_dir)\n",
    "\n",
    "merged_model = trainer.model.merge_and_unload()\n",
    "merged_model.save_pretrained(MERGED_DIR)\n",
    "tokenizer.save_pretrained(MERGED_DIR)\n",
    "print(f\"merged model saved to {MERGED_DIR}\")\n",
    "\n",
    "# Optional: push the merged model to the Hugging Face Hub. Requires an authenticated\n",
    "# session (huggingface_hub.login(token=HF_TOKEN) or `huggingface-cli login`).\n",
    "# HUB_REPO = \"your-username/lfm25-350m-nemotron-schema-grpo-merged\"\n",
    "# merged_model.push_to_hub(HUB_REPO)\n",
    "# tokenizer.push_to_hub(HUB_REPO)"
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