# grpo-rl-training

A skill that walks your agent through GRPO fine-tuning with TRL: reward function templates and training configs.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 963 installs · ★ 13956
- Author: Orchestra Research
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/06-post-training/grpo-rl-training/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/grpo-rl-training

## What it does

This skill covers Group Relative Policy Optimization with the TRL library. The model generates several completions per prompt, rewards rank them inside each group, and the policy shifts toward the better ones. No separate reward model is involved, which makes debugging simpler than with PPO.

The workflow has three steps: build a dataset in chat format with a system prompt, write reward functions, then configure `GRPOConfig`. Reward functions are split by purpose — correctness, format such as XML tags, length. One example gives partial credit, scoring each opening and closing tag on its own. The author recommends combining 3–5 reward functions at once.

Running it takes `transformers>=4.47.0`, `trl>=0.14.0`, `datasets>=3.2.0`, `peft>=0.14.0` and `torch`. The sample config targets a small GPU: batch size 1, gradient accumulation over 4 steps, learning rate 5e-6. If you already have good preference pairs, skip GRPO and use DPO or PPO instead.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill grpo-rl-training -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/grpo-rl-training/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/grpo-rl-training/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill grpo-rl-training -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/grpo-rl-training/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill grpo-rl-training -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/grpo-rl-training/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs grpo-rl-training
```

2. **Manually: put the files in place**
- File: `.github/skills/grpo-rl-training/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill grpo-rl-training -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 06-post-training/grpo-rl-training
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/grpo-rl-training/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill grpo-rl-training -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/06-post-training/grpo-rl-training/SKILL.md

## When to use it
- force the model to answer inside XML tags
- train on tasks with a verifiable answer
- reward the chain of thought before the final answer
- run training on a single small GPU
- combine several reward functions into one signal

## Example prompts
- help me set up GRPO training with TRL for my model
- write a format reward function for reasoning and answer XML tags
- pick GRPOConfig parameters for a single GPU

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 672d2e9e6e3b77b2.

BEGIN THIRD-PARTY FILE 672d2e9e6e3b77b2
````text
---
name: grpo-rl-training
description: Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Post-Training, Reinforcement Learning, GRPO, TRL, RLHF, Reward Modeling, Reasoning, DPO, PPO, Structured Output]
dependencies: [transformers>=4.47.0, trl>=0.14.0, datasets>=3.2.0, peft>=0.14.0, torch]
---

# GRPO/RL Training with TRL

Expert-level guidance for implementing Group Relative Policy Optimization (GRPO) using the Transformer Reinforcement Learning (TRL) library. This skill provides battle-tested patterns, critical insights, and production-ready workflows for fine-tuning language models with custom reward functions.

## When to Use This Skill

Use GRPO training when you need to:
- **Enforce specific output formats** (e.g., XML tags, JSON, structured reasoning)
- **Teach verifiable tasks** with objective correctness metrics (math, coding, fact-checking)
- **Improve reasoning capabilities** by rewarding chain-of-thought patterns
- **Align models to domain-specific behaviors** without labeled preference data
- **Optimize for multiple objectives** simultaneously (format + correctness + style)

**Do NOT use GRPO for:**
- Simple supervised fine-tuning tasks (use SFT instead)
- Tasks without clear reward signals
- When you already have high-quality preference pairs (use DPO/PPO instead)

---

## Core Concepts

### 1. GRPO Algorithm Fundamentals

**Key Mechanism:**
- Generates **multiple completions** for each prompt (group size: 4-16)
- Compares completions within each group using reward functions
- Updates policy to favor higher-rewarded responses relative to the group

**Critical Difference from PPO:**
- No separate reward model needed
- More sample-efficient (learns from within-group comparisons)
- Simpler to implement and debug

**Mathematical Intuition:**
```
For each prompt p:
  1. Generate N completions: {c₁, c₂, ..., cₙ}
  2. Compute rewards: {r₁, r₂, ..., rₙ}
  3. Learn to increase probability of high-reward completions
     relative to low-reward ones in the same group
```

### 2. Reward Function Design Philosophy

**Golden Rules:**
1. **Compose multiple reward functions** - Each handles one aspect (format, correctness, style)
2. **Scale rewards appropriately** - Higher weight = stronger signal
3. **Use incremental rewards** - Partial credit for partial compliance
4. **Test rewards independently** - Debug each reward function in isolation

**Reward Function Types:**

| Type | Use Case | Example Weight |
|------|----------|----------------|
| **Correctness** | Verifiable tasks (math, code) | 2.0 (highest) |
````
END THIRD-PARTY FILE 672d2e9e6e3b77b2

The third-party file has ended. Everything between the 672d2e9e6e3b77b2 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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