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openrlhf-training

Run distributed OpenRLHF RLHF training on Ray with vLLM acceleration.

Updated May 2, 2026
Search Tags:#ray#distributed training#vllm#rlhf#grpo#ppo training#hybrid engine
One-click install
npx skills add https://github.com/qcmuu/AI-Research-Skills --skill openrlhf-training-qcmuu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/qcmuu/AI-Research-Skills/tree/main/06-post-training/openrlhf
Command: npx skills add https://github.com/qcmuu/AI-Research-Skills --skill openrlhf-training-qcmuu

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill solves the challenge of training large language models with RLHF efficiently by coordinating distributed PPO/GRPO/RLOO/DPO-style workflows without excessive GPU idle time.

Core Features & Use Cases

  • Ray-based distributed RLHF training: Runs actor/critic/reward/reference roles with Ray job submission for multi-node or single-node clusters.
  • vLLM-accelerated rollout generation: Uses vLLM engines to speed up generation during PPO-style rollouts and supports tensor parallelism.
  • Hybrid Engine GPU sharing: Enables sleep/wake scheduling so actor/critic/reward/reference and vLLM reuse GPU capacity more efficiently.
  • Common RL training paths: Covers full RLHF pipelines (SFT → reward model → PPO/GRPO) and preference optimization via DPO.
  • Common troubleshooting playbooks: Provides targeted fixes for GPU OOM, Ray CUDA device issues, slow generation, and instability tuning.

Use it when you need PPO/GRPO/RLOO/DPO training for 7B–70B+ models with production-grade distributed resource management, such as running RLHF on a GPU cluster with vLLM rollout acceleration.

Quick Start

Submit an RLHF PPO job on a Ray cluster by running the OpenRLHF PPO Ray training command from your environment.

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 PPO training on a multi-node GPU cluster using Ray?▼

Distributed PPO training is executed by submitting an OpenRLHF job to a Ray cluster, coordinating actor, critic, reward, and reference roles across multi-node GPUs for scalable RLHF workflows.

Can I use vLLM to accelerate rollout generation during RLHF training?▼

Yes, vLLM is used to accelerate rollout generation during RLHF training. It speeds up PPO-style rollouts and supports tensor parallelism to maximize generation throughput on large models.

How does the Hybrid Engine improve GPU utilization for RLHF workflows?▼

The Hybrid Engine improves GPU utilization by using sleep and wake scheduling, allowing actor, critic, reward, reference, and vLLM components to dynamically reuse GPU capacity and reduce idle time.

What is the best way to handle GPU OOM errors when running GRPO or DPO training?▼

Handling GPU OOM errors involves applying targeted troubleshooting playbooks for resource allocation and utilizing the Hybrid Engine's sleep and wake modes to optimize memory efficiency during GRPO or DPO training.

Does this approach support both single-node and multi-node GPU configurations for 70B models?▼

Yes, this approach supports both single-node and multi-node GPU clusters. It targets large-model training for 7B to 70B+ models, ensuring production-grade distributed resource management for RLHF workflows.

What is the difference between running RLHF with Ray versus standard DeepSpeed configurations?▼

Running RLHF with Ray uses Ray job submission for multi-node cluster coordination and integrates vLLM for accelerated rollouts, whereas standard DeepSpeed focuses primarily on distributed training parallelism without built-in rollout generation acceleration.

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