skills.rest
lenadlm
lenadlmCOMMUNITY
Explore all skills by this author →
𝕏@lenadlmGitHub

grpo-rl-training

Implements GRPO for language model fine-tuning using TRL libraries.

Updated Oct 23, 2024
Search Tags:#TRL#RLHF#PPO#GRPO#DPO#Reward Modeling#Reasoning#Structured Output
One-click install
npx skills add https://github.com/lenadlm/docker --skill grpo-rl-training-lenadlm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grpo-rl-training
Source: https://github.com/lenadlm/docker/tree/main/hermes-skills/skills/mlops/training/grpo-rl-training
Command: npx skills add https://github.com/lenadlm/docker --skill grpo-rl-training-lenadlm

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. It offers battle-tested patterns, critical insights, and production-ready workflows for fine-tuning language models with custom reward functions.

Core Features & Use Cases

  • GRPO/RL Fine-tuning: Implements Group Relative Policy Optimization using TRL for reasoning and task-specific model training.
  • Reward Function Examples: Provides 20+ reward function examples with code for verifiable tasks and reasoning.
  • Hyperparameter Tuning Guide: Offers a guide to tuning hyperparameters for optimal training results.
  • Training Insights: Includes insights into loss behavior, metrics, and debugging.

Quick Start

Start by reading the SKILL.md document. Then, copy the basic_grpo_training.py file to begin your custom training.

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 language model fine-tuning with TRL?▼

GRPO fine-tuning with TRL requires configuring Group Relative Policy Optimization using the transformers, trl, datasets, peft, and torch libraries to train language models with custom reward functions. You can start by copying the basic training script provided.

What is the difference between GRPO and PPO for RLHF?▼

GRPO optimizes language models using group relative policy optimization without requiring a separate critic model, whereas PPO typically requires a value function. This Skill provides expert guidance on tuning GRPO hyperparameters and understanding loss behavior for reasoning tasks.

How do I design custom reward functions for verifiable reasoning tasks?▼

Designing custom reward functions for verifiable tasks involves creating specific evaluation logic for model outputs. This Skill provides over 20 reward function examples with code to guide the implementation of structured output and reasoning evaluations during training.

What dependencies do I need to run GRPO training scripts?▼

You need Python environments with transformers>=4.47.0, trl>=0.14.0, datasets>=3.2.0, peft>=0.14.0, and torch installed. These libraries provide the foundational transformer architectures and reinforcement learning utilities required for model training and evaluation.

Why does my GRPO training loss behave unexpectedly during fine-tuning?▼

Unexpected GRPO training loss often relates to hyperparameter misconfiguration or reward function scaling issues. This Skill includes training insights into loss behavior, metrics monitoring, and debugging techniques to help stabilize Group Relative Policy Optimization runs.

Can I use PEFT with TRL for Group Relative Policy Optimization?▼

Yes, you can use PEFT with TRL for GRPO training. The Skill explicitly lists peft>=0.14.0 as a dependency, enabling parameter-efficient fine-tuning methods like LoRA to reduce computational requirements during reinforcement learning workflows.

Related Skills

Explore More Software Engineering Skills→
xiaoquqixiaoquqi

fine-tuning-with-trl

Implement RLHF fine-tuning workflows with TRL for instruction and preference alignment.

Community
Advanced
attentiondotnetattentiondotnet

grpo-rl-training

Implement GRPO/TRL reinforcement learning fine-tuning workflows for language models.

Community
Advanced
eliottbusinesseliottbusiness

fine-tuning-with-trl

Fine-tune language models with TRL workflows like SFT, DPO, and PPO.

Community
Advanced
eddieluengeddielueng
1

fine-tuning-with-trl

Fine-tune language models with TRL for SFT, DPO, and PPO/GRPO workflows.

Community
Advanced