Can I use this skill for simple fine-tuning?

No, GRPO is designed for reinforcement learning with reward signals. For standard instruction following with labeled data, SFT (Supervised Fine-Tuning) is recommended.

Why does the loss increase during GRPO training?

In GRPO, the loss often represents the KL divergence from the initial policy. An increasing loss indicates the model is successfully diverging from its baseline to optimize for rewards.

What libraries are required for this skill?

This skill utilizes the TRL (Transformer Reinforcement Learning) library, alongside Hugging Face Transformers, PEFT, and optionally Unsloth for performance.

What is GRPO and how does it differ from PPO?

GRPO (Group Relative Policy Optimization) compares multiple completions within a group rather than using a separate reward model, making it more memory-efficient and simpler to implement than PPO.

GRPO RL Training Specialist

Name: GRPO RL Training Specialist
Author: Orchestra-Research

byOrchestra-Research

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3,983

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Data Science & ML

Implements Group Relative Policy Optimization (GRPO) for reasoning and task-specific model alignment using the TRL library.

This skill provides a comprehensive framework for fine-tuning language models through GRPO, a sample-efficient reinforcement learning technique that eliminates the need for a separate reward model. It equips users with battle-tested patterns for designing multi-objective reward functions, configuring memory-optimized training runs with LoRA and Unsloth, and monitoring critical metrics like KL divergence and reward stability. Whether you are enforcing strict XML formatting or improving mathematical reasoning, this skill transforms standard models into high-performance reasoning agents.

Key Features

01Integration with Unsloth and vLLM for 2-3x faster training speeds

02Memory-optimized configurations for single-GPU and multi-GPU setups

03Expert guidance on interpreting RL-specific training loss and metrics

04Standardized templates for dataset preparation and structured output

053,983 GitHub stars

06Advanced reward function design for format, correctness, and style

Use Cases

01Optimizing model performance for complex, multi-objective domain behaviors

02Training models to follow strict XML or JSON reasoning patterns

03Aligning models for verifiable tasks like math problems or code generation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add orchestra-research/ai-research-skills grpo-rl-training

For use in Claude.ai and ChatGPT

Download Skill