# Grpo Rl Training

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model...

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill grpo-rl-training -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill grpo-rl-training -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill grpo-rl-training -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill grpo-rl-training -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/06-post-training/grpo-rl-training/SKILL.md