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openrlhf-training

Train large language models with RLHF algorithms using Ray and vLLM.

11.5k|842|Updated Nov 3, 2025
Search Tags:#fine-tuning#ray#distributed training#ppo#vllm#rlhf#llm training
One-click install
npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill openrlhf-training-orchestra-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/06-post-training/openrlhf
Command: npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill openrlhf-training-orchestra-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openrlhf, ray, vllm, torch, transformers, deepspeed, and includes references (resource) components.

What problem does it solve?

This Skill streamlines the complex process of Reinforcement Learning from Human Feedback (RLHF) for large language models, enabling faster iteration and improved model performance.

Core Features & Use Cases

  • High-Performance RLHF: Supports PPO, GRPO, RLOO, DPO training for models up to 70B+ parameters.
  • Distributed Training: Leverages Ray for multi-node, multi-GPU training.
  • Inference Acceleration: Integrates vLLM for significantly faster generation during training rollouts.
  • Use Case: Fine-tune a Llama-3 8B model using PPO with a custom reward model to align its responses with specific safety guidelines, significantly reducing the time and computational resources required compared to traditional methods.

Quick Start

Use the openrlhf-training skill to start PPO training for a Llama-3 8B model using 8 GPUs.

Frequently Asked Questions about openrlhf-training

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

FAQPage Schema
How do I accelerate distributed RLHF training for large language models?▼

Distributed RLHF training for large language models is accelerated by integrating Ray for multi-node scaling and vLLM for fast inference rollouts, supporting models up to 70B+ parameters.

What algorithms are supported for fine-tuning LLMs with human feedback?▼

Fine-tuning LLMs with human feedback supports multiple algorithms including PPO, GRPO, RLOO, and DPO to align model responses with specific safety guidelines or custom reward models.

Can I use vLLM and Ray to scale PPO training across multiple GPUs?▼

Yes, you can use vLLM and Ray to scale PPO training across multiple GPUs, leveraging Ray for distributed multi-node execution and vLLM to significantly speed up generation during rollouts.

Do I need specific NVIDIA GPU hardware for RLHF training?▼

Yes, RLHF training requires specific NVIDIA GPU hardware and PyTorch container environments to achieve optimal performance when fine-tuning large language models up to 70B+ parameters.

What is the best way to fine-tune a Llama-3 8B model using PPO?▼

The best way to fine-tune a Llama-3 8B model using PPO is utilizing a high-performance RLHF framework that combines distributed Ray execution and vLLM inference acceleration to reduce computational resources.

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