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grpo-rl-training

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

1|Updated Feb 21, 2026
Search Tags:#llm#fine-tuning#reinforcement learning#trl#grpo#reward modeling
One-click install
npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill grpo-rl-training-tianhao909
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grpo-rl-training
Source: https://github.com/tianhao909/AI-Research-SKILLs-cn/tree/main/06-post-training/grpo-rl-training
Command: npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill grpo-rl-training-tianhao909

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 enables fine-tuning large language models (LLMs) using Group Relative Policy Optimization (GRPO) to align their outputs with specific, complex objectives beyond simple text prediction.

Core Features & Use Cases

  • Custom Reward Functions: Define and implement precise reward signals for format adherence, correctness, reasoning quality, and more.
  • Efficient Fine-Tuning: Utilizes TRL library for efficient GRPO training, reducing the need for large preference datasets.
  • Use Case: Train a model to generate mathematical solutions that not only provide the correct answer but also follow a specific step-by-step reasoning format, penalizing incorrect formatting or logical fallacies.

Quick Start

Use the grpo-rl-training skill to fine-tune the Qwen 2.5 1.5B Instruct model using the provided dataset and correctness and format 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 for format adherence?▼

You can fine-tune LLMs with custom reward functions using Group Relative Policy Optimization (GRPO) to align outputs with specific complex objectives like format adherence and reasoning quality.

What is the best way to align LLM outputs without needing large preference datasets?▼

GRPO training via the TRL library reduces the need for large preference datasets by efficiently optimizing custom objective alignment using user-defined reward signals.

Can I use PEFT and Hugging Face Transformers for efficient GRPO training?▼

Yes, GRPO training integrates with Hugging Face Transformers and PEFT to enable efficient fine-tuning of large language models on complex reasoning tasks.

How does GRPO reinforcement learning improve mathematical reasoning in LLMs?▼

GRPO uses custom reward functions to train models to generate mathematical solutions with correct answers and step-by-step reasoning, penalizing incorrect formatting or logical fallacies.

Does TRL support GRPO for verifiable correctness and specific output formats?▼

Yes, TRL supports GRPO to fine-tune large language models for complex tasks requiring specific output formats, verifiable correctness, and improved reasoning through custom reward functions.

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