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grpo-rl-training

Fine-tune language models with GRPO using the TRL library.

Updated Mar 12, 2026
Search Tags:#rl training#reward functions#trl#grpo#llm fine-tuning#model alignment
One-click install
npx skills add https://github.com/GarrettRoi/open-manus --skill grpo-rl-training-garrettroi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grpo-rl-training
Source: https://github.com/GarrettRoi/open-manus/tree/main/skills/mlops/training/grpo-rl-training
Command: npx skills add https://github.com/GarrettRoi/open-manus --skill grpo-rl-training-garrettroi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, trl, datasets, peft, torch, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the complex process of fine-tuning large language models using Group Relative Policy Optimization (GRPO) with the TRL library, enabling custom alignment and task-specific performance improvements.

Core Features & Use Cases

  • Custom Reward Functions: Define and implement precise reward signals for format, correctness, and style.
  • Efficient Fine-tuning: Leverages GRPO for sample-efficient learning without a separate reward model.
  • Use Case: Fine-tune a model to consistently generate code that adheres to specific style guides and passes unit tests, by rewarding both code correctness and adherence to formatting rules.

Quick Start

Use the grpo-rl-training skill to fine-tune a model using the provided template and reward functions.

Frequently Asked Questions about grpo-rl-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune an LLM with custom reward functions using GRPO?▼

To fine-tune an LLM with GRPO, use the TRL library to define custom reward functions for format, correctness, and style. This enables sample-efficient learning without requiring a separate reward model, directly aligning the model to your specific task outputs.

What is Group Relative Policy Optimization used for in LLM training?▼

Group Relative Policy Optimization (GRPO) is used for LLM alignment to enforce specific output formats and verifiable correctness. It provides sample-efficient fine-tuning by optimizing the model directly against user-defined reward signals without training a separate reward model.

Can I use TRL with PEFT and transformers for GRPO training?▼

Yes, GRPO training integrates directly with TRL, transformers, PEFT, and PyTorch. This stack supports efficient fine-tuning workflows, allowing you to apply parameter-efficient methods while implementing custom reward functions for model alignment.

What's the best way to align a model to pass unit tests and follow style guides?▼

The best way to align a model for code correctness and style is GRPO fine-tuning with TRL. You define reward functions that evaluate generated code against formatting rules and unit test results, directly optimizing the model to produce compliant outputs.

Do I need a separate reward model for RL training with GRPO?▼

No, you do not need a separate reward model for GRPO training. GRPO enables sample-efficient learning by leveraging user-defined reward functions directly, simplifying the fine-tuning pipeline and reducing infrastructure requirements.

How do I prepare datasets for GRPO fine-tuning with TRL?▼

Preparing datasets for GRPO involves formatting inputs for your specific task and defining reward functions to evaluate outputs. The skill provides scripts and templates to facilitate the dataset preparation workflow before initializing the TRL training process.

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