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grpo-rl-training

Guide GRPO/RL training with TRL using custom reward functions.

2|7|Updated Jun 19, 2026
Search Tags:#model training#reasoning#trl#rlhf#grpo#reward modeling
One-click install
npx skills add https://github.com/humanerd-drew/opencode-drewgent --skill grpo-rl-training-humanerd-drew
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grpo-rl-training
Source: https://github.com/humanerd-drew/opencode-drewgent/tree/main/skills/mlops/training/grpo-rl-training
Command: npx skills add https://github.com/humanerd-drew/opencode-drewgent --skill grpo-rl-training-humanerd-drew

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 fine-tuning language models with custom reward functions using Group Relative Policy Optimization (GRPO) with TRL for tasks that require reasoning and task-specific optimization.

Core Features & Use Cases

  • GRPO/RL Training: Offers complete GRPO concepts, implementation workflow, reward function examples, hyperparameter tuning, training insights, and troubleshooting.
  • Custom Reward Functions: Provides design philosophy, types, and implementation examples for creating diverse and robust reward functions.
  • Templates & Examples: Includes production-ready templates and extensive reward function examples to speed up development.
  • Use Cases: Ideal for tasks requiring specific output formats, verifiable tasks with objective correctness metrics, improving reasoning capabilities, and optimizing for multiple objectives.

Quick Start

To start using this skill, read the SKILL.md documentation first. Then, copy the basic_grpo_training.py from the templates/ directory and customize it for your task. Use the reward_functions_library.py for inspiration, and follow the step-by-step guide to train your model with custom 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 implement GRPO reinforcement learning training with TRL for a custom task?▼

Implement GRPO training with TRL by copying the basic training template, defining custom reward functions for your task, and tuning hyperparameters. The skill provides step-by-step workflows and production-ready scripts to guide the process.

What is Group Relative Policy Optimization used for in language model training?▼

Group Relative Policy Optimization (GRPO) is used for fine-tuning language models with custom reward signals. It optimizes models for specific output formats, verifiable outcomes, and improved reasoning capabilities without needing a separate value model.

How do I create custom reward functions for RLHF with TRL?▼

Create custom reward functions for TRL by designing rules that evaluate task-specific correctness and output formats. The skill provides a reward function library with diverse implementation examples to ensure robust model optimization.

Do I need PEFT and Transformers to run GRPO training?▼

Yes, GRPO training requires Transformers, TRL, datasets, PEFT, and PyTorch. These dependencies provide the foundational model architectures, dataset processing, and parameter-efficient fine-tuning capabilities needed for the workflow.

When should I use GRPO instead of standard RLHF for model fine-tuning?▼

Use GRPO instead of standard RLHF when your task has clear reward signals, requires specific output formats, or demands verifiable outcomes. It is ideal for optimizing reasoning capabilities and handling multiple objectives simultaneously.

Why is my GRPO model training not producing the expected reasoning outputs?▼

GRPO training issues often stem from poorly designed reward functions or incorrect hyperparameters. The skill offers expert troubleshooting insights, hyperparameter tuning guidance, and reward function design philosophy to resolve these bottlenecks.

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