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openrlhf-training

Orchestrate distributed RLHF training for PPO, GRPO, RLOO, and DPO workflows.

Updated Apr 3, 2026
Search Tags:#ray#multi-node#distributed-training#ppo#vllm#rlhf
One-click install
npx skills add https://github.com/handsomelong922/my-codex-skills --skill openrlhf-training-handsomelong922
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/handsomelong922/my-codex-skills/tree/main/skills/openrlhf
Command: npx skills add https://github.com/handsomelong922/my-codex-skills --skill openrlhf-training-handsomelong922

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

OpenRLHF-training addresses the complexity of building and running large-scale RLHF pipelines by providing a cohesive framework that orchestrates distributed training workflows, GPU sharing, and integration with Ray and vLLM.

Core Features & Use Cases

  • Distributed RLHF: Train PPO/GRPO/RLOO/DPO across multi-node GPU clusters with scalable resource management.
  • Hybrid Engine & vLLM acceleration: Efficient GPU utilization through co-located models and sleep/wake cycles.
  • Modular workflows: Easy to swap components (reward models, reference models, prompts) for experimentation.

Quick Start

Launch a distributed RLHF training run using OpenRLHF with Ray and vLLM.

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 across multi-node GPU clusters?▼

Hybrid GPU sharing optimizes efficient utilization during distributed RLHF training through co-located models and sleep/wake cycles, reducing idle time across multi-node GPU clusters.

Can I use vLLM with PPO and DPO workflows for large language model alignment?▼

Yes, vLLM integrates with PPO, GRPO, RLOO, and DPO workflows to accelerate large model alignment, leveraging Ray for scalable resource management across distributed GPU environments.

Does this framework support swapping reward models and reference models for experimentation?▼

The modular OpenRLHF framework supports easy component swapping, allowing you to interchange reward models, reference models, and prompts for flexible and scalable experimentation.

What is the best way to scale large-scale RLHF pipelines without running into GPU memory bottlenecks?▼

Scaling large-scale RLHF pipelines is handled by orchestrating distributed training workflows with Ray and vLLM, using hybrid GPU sharing and sleep/wake cycles to manage memory efficiently.

Do I need Ray to manage resources for multi-node distributed training?▼

Yes, Ray is required to provide scalable resource management and orchestration for multi-node GPU clusters when running distributed RLHF training workflows.

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