grpo-rl-training

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

Updated Mar 13, 2026

Search Tags:#llm #fine-tuning #reward functions #rl #trl #grpo

Ready to run

npx skills add https://github.com/hochoa13/Asistente-Harlest --skill grpo-rl-training-hochoa13

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: grpo-rl-training
Source: https://github.com/hochoa13/Asistente-Harlest/tree/main/skills/mlops/training/grpo-rl-training
Command: npx skills add https://github.com/hochoa13/Asistente-Harlest --skill grpo-rl-training-hochoa13

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and templates (resource) and examples (resource) components.

What problem does it solve?

This Skill streamlines the process of fine-tuning large language models using Group Relative Policy Optimization (GRPO) and custom reward functions, enabling precise control over model behavior and output quality.

Core Features & Use Cases

Customizable Fine-tuning: Train models to adhere to specific formats, improve reasoning, or enforce domain-specific behaviors using reward signals.
Efficient RL: Leverages GRPO for sample-efficient learning without requiring a separate reward model.
Use Case: Fine-tune a model to consistently generate code in a specific Pythonic style, rewarding adherence to PEP 8 guidelines and penalizing inefficient constructs.

Quick Start

Use the grpo-rl-training skill to fine-tune the Qwen2.5-1.5B-Instruct model on a custom dataset using provided 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 and custom reward functions, use the TRL library workflows provided by this Skill to apply Group Relative Policy Optimization. This enables precise control over model behavior and output quality without needing a separate reward model.

What is GRPO in reinforcement learning for language models?▼

GRPO, or Group Relative Policy Optimization, is an efficient RL technique for fine-tuning language models. It enables sample-efficient learning and precise model alignment through reward-driven signals without requiring a separate reward model.

Does GRPO require a separate reward model to improve LLM reasoning?▼

No, GRPO does not require a separate reward model to improve LLM reasoning. It leverages Group Relative Policy Optimization for sample-efficient learning and precise model alignment directly through custom reward functions.

Can I use TRL to train models for specific output formats and verifiable correctness?▼

Yes, you can use TRL with this Skill to train models for specific output formats and verifiable correctness. It provides implementation workflows for fine-tuning language models to enforce domain-specific behaviors using custom reward signals.

What's the best way to enforce a specific code style during LLM fine-tuning?▼

The best way to enforce a specific code style during LLM fine-tuning is using GRPO with custom reward functions. You can reward adherence to guidelines like PEP 8 and penalize inefficient constructs to achieve precise control over model behavior.

Are there limitations when using GRPO for sample-efficient learning?▼

GRPO is designed for sample-efficient learning without a separate reward model, making it suitable for advanced users achieving precise model alignment. However, it requires carefully designing custom reward functions to enforce domain-specific behaviors and output formats effectively.