Grpo Rl Training

This is comprehensive guidance for fine-tuning language models with Group Relative Policy Optimization using the TRL library. You'd reach for this when you need to enforce structured outputs (XML tags, JSON) or improve reasoning on verifiable tasks like math or coding, where you have clear reward signals but not preference pairs. The skill walks through the full workflow: preparing chat-formatted datasets, composing multiple reward functions (correctness, format, style), and tuning hyperparameters like group size and learning rate. The reward function examples are especially practical, showing both binary scoring and incremental rewards with partial credit. Worth noting that Orchestra Research includes honest guidance about when not to use GRPO, which saves you from going down the wrong path on supervised tasks.

skills.sh installs: 1K
GitHub stars: 14.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add orchestra-research/ai-research-skills --skill grpo-rl-training --agent claude-code

Installs into .claude/skills of the current project.

Files

GRPO/RL Training with TRL

Expert-level guidance for implementing Group Relative Policy Optimization (GRPO) using the Transformer Reinforcement Learning (TRL) library. This skill provides battle-tested patterns, critical insights, and production-ready workflows for fine-tuning language models with custom reward functions.

When to Use This Skill

Use GRPO training when you need to:

Enforce specific output formats (e.g., XML tags, JSON, structured reasoning)
Teach verifiable tasks with objective correctness metrics (math, coding, fact-checking)
Improve reasoning capabilities by rewarding chain-of-thought patterns
Align models to domain-specific behaviors without labeled preference data
Optimize for multiple objectives simultaneously (format + correctness + style)

Do NOT use GRPO for: