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grpo-rl-training

Fine-tune language models with GRPO and TRL for verifiable tasks.

Updated Mar 29, 2026
Search Tags:#Code Generation#RLHF#GRPO#Reward Modeling#Reasoning
One-click install
npx skills add https://github.com/shuff57/agent-evo --skill grpo-rl-training-shuff57
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grpo-rl-training
Source: https://github.com/shuff57/agent-evo/tree/main/skills/.archive/topics-2026-05-10/mlops/training/grpo-rl-training
Command: npx skills add https://github.com/shuff57/agent-evo --skill grpo-rl-training-shuff57

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 enables fine-tuning language models for specific, verifiable tasks, such as math reasoning, code generation, and structured output, using Group Relative Policy Optimization (GRPO) and Transformer Reinforcement Learning (TRL).

Core Features & Use Cases

  • GRPO/RL Fine-Tuning: Leverages GRPO for sample-efficient learning from within-group comparisons.
  • Reward Function Design: Offers a comprehensive guide for creating and combining reward functions for various aspects like correctness, format, and style.
  • Training Workflows: Provides a structured workflow for dataset preparation, reward function implementation, and model setup.
  • Use Case: Use this Skill to teach a language model to generate accurate code by providing a dataset of code examples and corresponding rewards that focus on correctness and formatting.

Quick Start

Use the grpo-rl-training skill to train a language model for code generation by providing a dataset of code examples and corresponding 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 a language model for code generation using GRPO?▼

Fine-tune a language model for code generation using GRPO by providing a dataset of code examples and implementing reward functions for correctness and formatting. This Skill leverages Group Relative Policy Optimization to enable sample-efficient learning from within-group comparisons.

What is Group Relative Policy Optimization and when should I use it for RLHF?▼

Group Relative Policy Optimization (GRPO) is a reinforcement learning technique for sample-efficient learning from within-group comparisons. Use GRPO for RLHF when fine-tuning language models for verifiable tasks like math reasoning, code generation, and structured output.

Can I use TRL and transformers to train models for verifiable math reasoning?▼

Yes, you can use TRL and transformers to train models for verifiable math reasoning. This Skill implements GRPO with Transformer Reinforcement Learning specifically for tasks requiring verifiable outputs, including math and structured reasoning.

How do I design reward functions for RL training with structured outputs?▼

Design reward functions for RL training by creating and combining functions for correctness, format, and style. This Skill provides a comprehensive guide and structured workflow for implementing reward functions alongside dataset preparation and model setup.

Do I need peft and torch to run GRPO training workflows?▼

Yes, you need peft, torch, transformers, trl, and datasets to run GRPO training workflows. These libraries are required dependencies for handling reward modeling, reinforcement learning, and language model training.

Why use GRPO instead of standard RLHF for language model training?▼

Use GRPO instead of standard RLHF for sample-efficient learning through within-group comparisons. It is specifically designed for verifiable tasks like coding and math reasoning, whereas standard RLHF lacks this optimized group-based comparative advantage.

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