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grpo-rl-training

Implement GRPO fine-tuning of language models with TRL and custom reward functions.

Updated Apr 15, 2026
Search Tags:#fine-tuning#TRL#GRPO#language model#reward function#reforcement learning
One-click install
npx skills add https://github.com/HuskyDanny/hermes-agent-config --skill grpo-rl-training-huskydanny
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grpo-rl-training
Source: https://github.com/HuskyDanny/hermes-agent-config/tree/main/skills/mlops/training/grpo-rl-training
Command: npx skills add https://github.com/HuskyDanny/hermes-agent-config --skill grpo-rl-training-huskydanny

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 provides expert-level guidance for implementing Group Relative Policy Optimization (GRPO) using the Transformer Reinforcement Learning (TRL) library, enabling fine-tuning language models with custom reward functions for various tasks like reasoning and task-specific model training.

Core Features & Use Cases

  • GRPO/RL Training: Implements GRPO for reinforcement learning tasks, using TRL for fine-tuning.
  • Reward Function Design: Offers guidelines for designing reward functions, including correctness, format, length, and style rewards.
  • Training Configuration: Provides configuration options for different training scenarios, including memory-optimized and high-performance settings.
  • Model Setup and Training: Demonstrates how to set up and train models using TRL, including loading models, setting up tokenizer, and initializing the trainer.
  • Critical Training Insights: Offers insights into loss behavior, reward tracking, and common pitfalls.
  • Advanced Patterns: Explores multi-stage training, adaptive reward scaling, and custom dataset integration.
  • Deployment and Inference: Covers saving and merging LoRA, inference example, and best practices for deployment.
  • Troubleshooting Guide: Provides a debugging workflow and quick fixes for common issues.
  • Usage Instructions for Agents: Offers instructions for using the skill with agents.

Quick Start

Read the SKILL.md documentation to understand the core concepts and implementation workflow. Start with the basic_grpo_training.py template and adapt it for your specific task by modifying the dataset, reward functions, and system prompt.

Frequently Asked Questions about grpo-rl-training

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

FAQPage Schema
How do I implement GRPO for fine-tuning a language model with custom reward functions?▼

To implement GRPO, use the TRL library to set up the trainer, load your model and tokenizer, define custom reward functions for correctness or format, and configure training parameters for reinforcement learning fine-tuning.

What types of reward functions can I design for GRPO reinforcement learning training?▼

GRPO reward functions can evaluate correctness, format, length, and style. You can also apply adaptive reward scaling and integrate custom datasets to guide the model toward specific reasoning or task-specific behaviors.

Do I need specific versions of transformers and trl to run GRPO training?▼

Yes, GRPO training requires transformers version 4.47.0 or higher and trl version 0.14.0 or higher, alongside the datasets, peft, and torch libraries to ensure compatibility with the reinforcement learning setup.

Why does my GRPO training loss behave unexpectedly or reward tracking fail?▼

Unexpected loss behavior or reward tracking failures often stem from misconfigured reward functions or training parameters. Follow the debugging workflow and apply quick fixes for common pitfalls like memory optimization or adaptive scaling.

Can I use LoRA for memory-optimized GRPO training and deploy the merged model?▼

Yes, you can configure memory-optimized GRPO training using LoRA through the peft library, then save and merge the LoRA weights before deployment to run inference with the fine-tuned language model.

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