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openrlhf-training

Trains LLMs with RLHF algorithms (PPO, GRPO, RLOO, and DPO using Ray and vLLM.

Updated Aug 23, 2026
Search Tags:#deep learning#ray#distributed training#ppo#vllm#rlhf#llm training
One-click install
npx skills add https://github.com/choice5346/BiSHE --skill openrlhf-training-choice5346
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/choice5346/BiSHE/tree/main/.github/skills/openrlhf
Command: npx skills add https://github.com/choice5346/BiSHE --skill openrlhf-training-choice5346

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 process of training large language models (7B-70B+) using Reinforcement Learning from Human Feedback (RLHF), offering significant speed and efficiency improvements over traditional methods.

Core Features & Use Cases

  • High-Performance RLHF: Supports PPO, GRPO, RLOO, and DPO algorithms with Ray and vLLM acceleration.
  • Distributed Training: Optimized for multi-node GPU clusters, leveraging Ray for seamless scaling.
  • Efficient Inference: Integrates vLLM for up to 2x faster inference compared to DeepSpeedChat.
  • Use Case: Fine-tune a Llama-3 70B model using PPO with a custom reward model on a multi-node GPU cluster, achieving faster convergence and higher quality results.

Quick Start

Launch a PPO training job for Llama-3 8B using the provided command.

Frequently Asked Questions about openrlhf-training

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

FAQPage Schema
How do I accelerate RLHF training for large language models like Llama-3 70B?▼

To accelerate RLHF training for 70B+ models, use Ray for distributed multi-node scaling and vLLM for optimized inference. This combination achieves up to 2x faster inference compared to DeepSpeedChat.

Does vLLM improve inference speed during PPO training compared to DeepSpeedChat?▼

Yes, integrating vLLM for inference during PPO training achieves up to 2x faster speeds compared to DeepSpeedChat. This optimization significantly improves overall RLHF training efficiency.

Can I use Ray for distributed RLHF training on multi-node GPU clusters?▼

Ray facilitates high-performance distributed RLHF training by seamlessly scaling across multi-node GPU clusters. It works alongside ZeRO-3 sharding to maximize resource utilization for large models.

What reinforcement learning algorithms are supported for LLM training besides PPO?▼

Besides PPO, the RLHF training supports GRPO, RLOO, and DPO algorithms. These options are fully integrated with Ray and vLLM acceleration for optimized large language model performance.

Do I need ZeRO-3 sharding and DeepSpeed for training 70B parameter models?▼

ZeRO-3 sharding is supported to maximize memory efficiency when training 70B parameter models. DeepSpeed is included as a dependency to enable this advanced sharding capability alongside hybrid engine GPU sharing.

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