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grpo-rl-training

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

1|Updated Feb 1, 2026
Search Tags:#RLHF#GRPO#reward modeling#language model fine-tuning#Transformer Reinforcement Learning
One-click install
npx skills add https://github.com/safzanpirani/pi-config --skill grpo-rl-training-safzanpirani
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grpo-rl-training
Source: https://github.com/safzanpirani/pi-config/tree/main/skills/grpo-rl-training
Command: npx skills add https://github.com/safzanpirani/pi-config --skill grpo-rl-training-safzanpirani

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) components.

What problem does it solve?

This Skill provides expert-level guidance for fine-tuning language models with custom reward functions using Group Relative Policy Optimization (GRPO) and Transformer Reinforcement Learning (TRL), enhancing reasoning and task-specific model training.

Core Features & Use Cases

  • Expert-Level GRPO/RL Fine-Tuning: Implement Group Relative Policy Optimization (GRPO) for language models.
  • Reward Function Design: Utilize a comprehensive set of reward function examples and best practices for designing effective reward functions.
  • Training and Deployment: Learn how to train models using various configurations and deploy them for inference.

Quick Start

To get started, copy the 'basic_grpo_training.py' template to your project and customize it for your use case.

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 Group Relative Policy Optimization for language model fine-tuning?▼

Group Relative Policy Optimization (GRPO) for language model fine-tuning is implemented using the Transformer Reinforcement Learning (TRL) library, which requires Transformers, Datasets, PEFT, and PyTorch dependencies to execute the training scripts.

How do I design custom reward functions for RLHF training?▼

Designing custom reward functions for RLHF training involves applying best practices and utilizing comprehensive examples provided by the Skill, enabling effective task-specific model training and enhanced reasoning capabilities.

Does TRL support GRPO training with PEFT and Transformers?▼

TRL supports GRPO training with PEFT and Transformers, requiring specific versions including transformers>=4.47.0, trl>=0.14.0, datasets>=3.2.0, and peft>=0.14.0 to ensure compatibility during model training.

What is the best way to start fine-tuning a language model with custom reward functions?▼

The best way to start fine-tuning a language model with custom reward functions is to copy the 'basic_grpo_training.py' template into your project, customize it for your specific use case, and configure the training parameters.

When should I use GRPO over other reinforcement learning techniques for language models?▼

You should use GRPO over other reinforcement learning techniques for language models when your tasks require enhanced reasoning and you need to implement custom reward functions for task-specific model training.

What are the limitations of using GRPO for Transformer Reinforcement Learning?▼

Limitations of using GRPO for Transformer Reinforcement Learning include the strict dependency requirements for specific library versions and the necessity to design effective custom reward functions to achieve desired training outcomes.

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