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grpo-rl-training

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

2|Updated Feb 4, 2026
Search Tags:#reinforcement learning#trl#grpo#llm fine-tuning#reward modeling#customizable training
One-click install
npx skills add https://github.com/zhuangbiaowei/smart_bot --skill grpo-rl-training-zhuangbiaowei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grpo-rl-training
Source: https://github.com/zhuangbiaowei/smart_bot/tree/main/skills/grpo-rl-training
Command: npx skills add https://github.com/zhuangbiaowei/smart_bot --skill grpo-rl-training-zhuangbiaowei

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers>=4.47.0, trl>=0.14.0, datasets>=3.2.0, peft>=0.14.0, torch, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the process of fine-tuning large language models (LLMs) using Group Relative Policy Optimization (GRPO) with the TRL library, enabling precise control over model behavior for complex reasoning and task-specific outputs.

Core Features & Use Cases

  • GRPO Implementation: Provides a robust framework for GRPO training, which is more sample-efficient than PPO and doesn't require a separate reward model.
  • Custom Reward Functions: Offers a library of pre-built and customizable reward functions for correctness, format adherence, length, and style.
  • Production-Ready Templates: Includes minimal, production-ready training scripts and examples for various tasks like math reasoning, code generation, and summarization.
  • Use Case: Fine-tune an LLM to consistently generate well-formatted, mathematically correct answers to complex word problems by defining specific reward functions for structure and accuracy.

Quick Start

Use the grpo-rl-training skill to fine-tune a model using the basic_grpo_training.py template.

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?▼

You can fine-tune an LLM with GRPO by using the TRL library to define custom reward functions for correctness and format adherence, then executing production-ready training scripts like the basic template provided.

What is the difference between GRPO and PPO for reinforcement learning?▼

GRPO is more sample-efficient than PPO and does not require a separate reward model, streamlining the reinforcement learning process for language models while maintaining precise behavioral control.

Can I use TRL and transformers to align models for specific output formats?▼

Yes, TRL and transformers support aligning models to specific output formats by creating custom reward functions that enforce structure, improve reasoning, and match domain-specific requirements.

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

No, GRPO training does not require a separate reward model, as it uses Group Relative Policy Optimization to directly evaluate and align model outputs without additional reward modeling components.

What dependencies are required for GRPO reinforcement learning fine-tuning?▼

Required dependencies for GRPO fine-tuning include transformers, trl, datasets, peft, and torch, which collectively support the training scripts and model optimization workflows.

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