GRPO Reinforcement Fine-Tuning
Guides an agent through GRPO reinforcement fine-tuning of language models with TRL and custom reward functions
- Repository
- Orchestra-Research/AI-Research-SKILLs
- Stars
- 14,195
- Licence
- MIT
- Files
- 4 files
What it does
This skill gives an AI agent expert know-how for fine-tuning a language model with GRPO (Group Relative Policy Optimization), a reinforcement learning method. The model generates several answers per question, a scoring function rates them (for example for correct math answers or a required output format), and training nudges the model toward the higher-scoring answers. The skill supplies ready reward-function patterns, a dataset format, training settings, and a full example training script based on the TRL library.
Great for
- ML engineers who want to train a small open model to follow a strict output format or solve verifiable tasks like math
- Researchers experimenting with reward design for reasoning models
- Developers who already have a clear, automatically checkable definition of a good answer
Skip it if
- Simple instruction or style tuning with labelled examples, where supervised fine-tuning is the simpler choice (the skill itself says so)
- Tasks with no objective reward signal, since the reward functions would be arbitrary and the training could drift badly
- Running the example on a laptop or without a suitable GPU, as training a model requires significant compute and may incur cloud costs
Try asking
- “Help me set up GRPO training to make a small Qwen model answer GSM8K math problems in a reasoning and answer XML format”
- “Write a reward function that gives partial credit for correct XML tags in model outputs”
- “Adapt the basic GRPO training template to my own question-and-answer dataset”
- “Explain how group size and learning rate affect my GRPO training run”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 4 original files, the MIT licence and an attribution note.
grpo-rl-training.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip grpo-rl-training.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip grpo-rl-training.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Start with a small model and a few training steps to verify the reward functions behave as expected before committing to a long run
- Test each reward function in isolation on sample completions, as the skill recommends, before combining them
- Review the training script and its dataset and model downloads before executing it, since it trains a model and fetches data from the internet
What's inside
- examples/reward_functions_library.py
- README.md
- SKILL.md
- templates/basic_grpo_training.py
Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.