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openrlhf-training

Orchestrate distributed RLHF training with PPO/GRPO/RLOO/DPO across Ray clusters.

Updated Mar 30, 2026
Search Tags:#ray#multi-node#distributed-training#reinforcement-learning#ppo#vllm#rlhf
One-click install
npx skills add https://github.com/KappTech88/AI-RESEARCH-SKILLS-MCP --skill openrlhf-training-kapptech88
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/KappTech88/AI-RESEARCH-SKILLS-MCP/tree/main/skills/openrlhf
Command: npx skills add https://github.com/KappTech88/AI-RESEARCH-SKILLS-MCP --skill openrlhf-training-kapptech88

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

OpenRLHF enables high-performance RLHF training for large language models using distributed Ray clusters and vLLM acceleration, reducing training time and GPU idle.

Core Features & Use Cases

  • Distributed RLHF training with PPO/GRPO/RLOO/DPO in a single framework
  • Ray-based multi-node orchestration with vLLM acceleration and hybrid engine sleep modes
  • Supports large models (7B-70B) with efficient GPU sharing and checkpointing
  • Use case: Fine-tuning a 70B model using PPO with 4-8 nodes and 16 GPUs per node

Quick Start

Install dependencies, configure a Ray cluster, and run the PPO training workflow across your GPUs.

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 using Ray and vLLM?▼

Distributed RLHF training is orchestrated across Ray clusters with vLLM acceleration, utilizing hybrid GPU sharing and sleep modes to reduce training time and GPU idle for 7B-70B parameter large language models.

Can I fine-tune a 70B model with PPO across multiple nodes and GPUs?▼

Yes, you can fine-tune a 70B model using PPO with multi-node hardware configurations, scaling across 4-8 nodes with 16 GPUs per node, while Ray orchestrates the distributed workload and vLLM handles acceleration.

What reinforcement learning algorithms are supported for distributed LLM fine-tuning?▼

The framework supports multiple reinforcement learning algorithms for distributed LLM fine-tuning, including PPO, GRPO, RLOO, and DPO, allowing flexible algorithm selection within a single Ray-based orchestration environment.

Does vLLM acceleration work with Ray clusters for PPO training?▼

vLLM acceleration works directly with Ray clusters during PPO training, providing efficient GPU sharing and utilizing hybrid engine sleep modes to minimize resource idle time across multi-node distributed hardware configurations.

What's the best way to manage checkpoints during multi-node LLM fine-tuning?▼

The framework provides advanced checkpointing for multi-node LLM fine-tuning, integrating with Ray clusters to express extensible and reproducible workflows through clear dependency management scripts and modular components.

Why use OpenRLHF for distributed reinforcement learning instead of other frameworks?▼

OpenRLHF combines Ray-based multi-node orchestration with vLLM acceleration for distributed reinforcement learning, specifically targeting large 7B-70B models to reduce training time and GPU idle through hybrid GPU sharing.

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