grpo-rl-training

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training

by Orchestra-Research 12k GitHub stars MIT Updated Jun 16, 2026
grpo-rl-training skill by Orchestra-Research
grpo-rl-training — Productivity skill by Orchestra-Research

Overview

grpo-rl-training is an open-source productivity skill for Claude Code and compatible agents, published by Orchestra-Research. Its author describes it as: “Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training”. The project has 12k stars on GitHub and is available under the MIT license. Add it to your setup with `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`.

What grpo-rl-training does

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.

What’s inside

The SKILL.md for grpo-rl-training is organised into these sections:

  • When to Use This Skill
  • Core Concepts
  • 1. GRPO Algorithm Fundamentals
  • 2. Reward Function Design Philosophy
  • Implementation Workflow
  • Step 1: Dataset Preparation
  • Step 2: Reward Function Implementation
  • Step 3: Training Configuration
  • Step 4: Model Setup and Training
  • Critical Training Insights
  • 1. Loss Behavior (EXPECTED PATTERN)
  • 2. Reward Tracking

Installation

Add grpo-rl-training to your agent with:

/plugin marketplace add Orchestra-Research/AI-Research-SKILLs

Review a skill’s source before installing it — skills can instruct your agent to run commands. This command comes from the skill’s public repository, which is the source of truth for setup steps. New to skills? Read how to install Claude Code skills.

Supported agents

  • Claude Code — ships a claude code plugin marketplace
  • Codex — declared in the repository topics

License

grpo-rl-training is published under the MIT license.

Strengths

  • Clear MIT license — safe to read and adapt within its terms
  • Ships in Orchestra-Research/AI-Research-SKILLs, an established project with 11,807 GitHub stars
  • Works with Claude Code, Codex

Author

Orchestra-Research 41 skills in the catalog · GitHub profile ↗
View skills

Topics

Alternatives to grpo-rl-training

Similar skills from other repositories:

Need a skill like this, tailored to your team?Build a custom skill →

Frequently asked questions

What does grpo-rl-training do?
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
How do I install grpo-rl-training?
Run /plugin marketplace add Orchestra-Research/AI-Research-SKILLs in your agent, then reload your skills. Review the source at https://github.com/Orchestra-Research/AI-Research-SKILLs before installing.
Is grpo-rl-training free to use?
Yes. grpo-rl-training is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.
Which agents does grpo-rl-training work with?
grpo-rl-training lists Claude Code and Codex — named in its SKILL.md, its plugin marketplace or its repository topics. Skills use the portable SKILL.md format, so other agents that read that format may load it too.
Where does grpo-rl-training come from?
grpo-rl-training ships inside Orchestra-Research/AI-Research-SKILLs, a repository that contains 41 catalogued skills in total. The repository's 11,807 GitHub stars apply to that whole collection, not to this skill on its own.

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

Claude CodeCodex
12k GitHub stars MIT Updated Jun 16, 2026 Free

High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

Claude CodeCodex
12k GitHub stars MIT Updated Jun 16, 2026 Free

Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

Claude CodeCodex
12k GitHub stars MIT Updated Jun 16, 2026 Free

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

Claude CodeCodex
12k GitHub stars MIT Updated Jun 16, 2026 Free

Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.

Claude CodeCodex
12k GitHub stars MIT Updated Jun 16, 2026 Free