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openrlhf-training

Run distributed RLHF training for large language models on Ray clusters.

1.0k|117|Updated Feb 26, 2026
Search Tags:#ray#distributed-training#ppo#vllm#rlhf#grpo#openrlhf
One-click install
npx skills add https://github.com/OpenLAIR/dr-claw --skill openrlhf-training-openlair
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/post-training/openrlhf
Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill openrlhf-training-openlair

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

OpenRLHF enables high-performance, distributed RLHF training for large language models, orchestrating multiple components across nodes.

Core Features & Use Cases

  • Distributed PPO/GRPO/DPO/RLOO workflows with Ray and vLLM acceleration.
  • Multi-node scalability for 7B-70B+ models using ZeRO-3 and hybrid engine sleep modes.
  • End-to-end RLHF pipelines including reward modeling, policy optimization, and evaluation in production environments.

Quick Start

Launch a multi-node OpenRLHF training job on a Ray cluster to start PPO-based RLHF with vLLM acceleration.

Frequently Asked Questions about openrlhf-training

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

FAQPage Schema
How do I run distributed RLHF training for large language models with Ray and vLLM?▼

Distributed RLHF training for large language models is orchestrated through a Ray-based, vLLM-accelerated OpenRLHF workflow. It scales PPO, GRPO, DPO, and RLOO pipelines across multi-node environments for 7B-70B+ parameter models using ZeRO-3 and DeepSpeed integration.

What do I need to set up before starting PPO or GRPO training on a Ray cluster?▼

Before starting PPO or GRPO training, you need a functioning Ray cluster, configured vLLM engines, and ZeRO-3/DeepSpeed integration. Proper cluster resource configuration is required to orchestrate reward and reference components across multiple nodes.

Does OpenRLHF support multi-node scaling for 70B parameter models?▼

Yes, OpenRLHF supports multi-node scalability for 7B-70B+ models. It achieves this through ZeRO-3 integration and hybrid engine sleep modes, optimizing memory usage during high-performance distributed RLHF training.

Can I use vLLM to accelerate reward modeling and policy optimization?▼

Yes, vLLM accelerates end-to-end RLHF pipelines including reward modeling and policy optimization. The workflow leverages vLLM engines alongside Ray to optimize inference and training workloads in production environments.

Why use Ray and vLLM for RLHF instead of standard distributed training frameworks?▼

Using Ray and vLLM for RLHF enables high-performance orchestration of multiple components across nodes, which standard frameworks lack. It combines vLLM inference acceleration with Ray's flexible distributed computing for efficient 7B-70B+ model training.

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