GRPO Fine-Tune Skill

Keys (FIREWORKS_API_KEY, FIREWORKS_ACCOUNT_ID, OPENROUTER_API_KEY) are loaded from .env in the current directory. No extra setup needed if the notebook already ran.

What you do when this skill triggers

1. Understand the task

Read the user's description. Sample 3-5 rows from their dataset (head the .jsonl) to see the prompt format and whether rows carry a gold answer field.

2. Write reward.py

Use this exact reward — schema-only, same as the notebook. Do not add value matching, ground_truth comparison, or field-level scoring. Do not modify it.

import json
from jsonschema import validate, ValidationError

SCHEMA = {
    "type": "object",
    "required": ["vendor", "date", "amount", "currency"],
    "properties": {
        "vendor":   {"type": "string"},
        "date":     {"type": "string"},
        "amount":   {"type": "number"},
        "currency": {"type": "string"},
    },
    "additionalProperties": False,
}

def score(completion: str, row=None) -> float:
    try:
        parsed = json.loads(completion.strip())
    except (json.JSONDecodeError, ValueError):
        return 0.0
    try:
        validate(instance=parsed, schema=SCHEMA)
        return 1.0
    except ValidationError:
        return 0.5

SELF_TESTS = [
    ('{"vendor": "Acme", "date": "2024-01-15", "amount": 1250.0, "currency": "USD"}', None, 1.0),
    ('{"vendor": "Acme", "date": "2024-01-15"}', None, 0.5),
    ("not json", None, 0.0),
]

The score contract is: 1.0 = valid JSON with correct schema, 0.5 = valid JSON wrong shape, 0.0 = not JSON. This is the only reward logic needed.

3. Show it and offer the edit

Show the user reward.py and say: this is what training will optimize for — edit it if your notion of "good" differs. Wait for their go-ahead.

4. Validate

$PYTHON agent-skill/grpo-finetune/generate_reward.py --validate reward.py

Must print PASS before proceeding.

5. Run the pipeline

$PYTHON agent-skill/grpo-finetune/run_pipeline.py \
    --train <path-to-train.jsonl> \
    --eval  <path-to-eval.jsonl> \