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grpo-rl-training

Configure and run TRL-based GRPO fine-tuning with reward functions.

27|2|Updated Jan 15, 2024
Search Tags:#fine-tuning#alignment#rl#trl#grpo#reward-modeling
One-click install
npx skills add https://github.com/erfanzar/Xerxes-Agents --skill grpo-rl-training-erfanzar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grpo-rl-training
Source: https://github.com/erfanzar/Xerxes-Agents/tree/main/src/python/xerxes/skills/training/grpo-rl-training
Command: npx skills add https://github.com/erfanzar/Xerxes-Agents --skill grpo-rl-training-erfanzar

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

GRPO/RL training with TRL enables reproducible, reward-guided fine-tuning of language models using group-relative policy optimization, improving alignment and task-specific performance.

Core Features & Use Cases

  • Structured GRPO workflow for reward-based fine-tuning with multiple completions per prompt.
  • Templates and reward function patterns that support correctness, format, and style.
  • Production-ready training templates and examples to accelerate deployment.

Quick Start

Load your dataset, configure a GRPOTrainer with the supplied rewards, and start training with the included template.

Frequently Asked Questions about grpo-rl-training

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

FAQPage Schema
How do I set up GRPO training with TRL for LLM alignment?▼

GRPO training with TRL enables reproducible, reward-guided fine-tuning of language models using group-relative policy optimization, improving alignment and task-specific performance.

What is group-relative policy optimization used for in fine-tuning?▼

Group-relative policy optimization is used for reward-guided fine-tuning of language models, improving alignment and task-specific performance across custom datasets and evaluation scenarios.

Can I use custom reward modeling functions for formatting and reasoning with TRL?▼

Yes, the workflow provides reward function patterns that support correctness, format, and style, allowing you to guide policy optimization for reasoning and task-specific alignment.

Does GRPO fine-tuning require multiple completions per prompt?▼

Yes, the structured GRPO workflow for reward-based fine-tuning requires multiple completions per prompt to compute group-relative policy optimization for language model alignment.

What's the best way to prepare datasets for reward-based RL training?▼

Use the included data preparation templates to format your custom datasets, ensuring they match the TRL-compatible setup required for structured reward signals and GRPOTrainer execution.

When should I use GRPO instead of standard supervised fine-tuning?▼

Use GRPO when you need group-relative policy optimization for reasoning, formatting, and task-specific alignment across custom datasets, rather than standard supervised fine-tuning methods.

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