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.
grpo-rl-training
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.
Installation
Add grpo-rl-training to your agent with:
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs Always review a skill's source before installing it. This command comes from the skill's public repository; the linked repo is the source of truth for exact setup steps.
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
When to use it
Reach for grpo-rl-training when you want productivity help from your agent without writing the same instructions every session. Load the skill and the agent picks it up automatically for relevant tasks.
Strengths
- Clear MIT license — safe to read and adapt
- Ships in Orchestra-Research/AI-Research-SKILLs, an established project with 11,807 GitHub stars
- Actively maintained (recent commits)
Topics
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.
- 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.
Related skills
More Productivity →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.
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.
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.
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.