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openrlhf-training

Train large language models with RLHF algorithms using Ray and vLLM.

2|Updated Feb 4, 2026
Search Tags:#deep learning#ray#distributed training#large language models#vllm#rlhf
One-click install
npx skills add https://github.com/zhuangbiaowei/smart_bot --skill openrlhf-training-zhuangbiaowei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/zhuangbiaowei/smart_bot/tree/main/skills/openrlhf
Command: npx skills add https://github.com/zhuangbiaowei/smart_bot --skill openrlhf-training-zhuangbiaowei

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 complex process of Reinforcement Learning from Human Feedback (RLHF) for large language models, making advanced training techniques accessible and efficient.

Core Features & Use Cases

  • High-Performance RLHF: Utilizes Ray and vLLM for distributed training and accelerated inference.
  • Multiple Algorithms: Supports PPO, GRPO, RLOO, and DPO training paradigms.
  • Use Case: Fine-tune a 70B parameter model using PPO with distributed training across multiple nodes, leveraging vLLM for faster sample generation and DeepSpeed ZeRO-3 for memory efficiency.

Quick Start

Install OpenRLHF with vLLM support by running pip install openrlhf[vllm].

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?▼

To accelerate RLHF training for large language models, you can use distributed computing with Ray and vLLM for fast inference generation. This framework supports PPO, GRPO, RLOO, and DPO algorithms across multi-node environments.

Can I fine-tune a 70B model using PPO with distributed training?▼

Yes, you can fine-tune a 70B parameter model using PPO with distributed training. The framework uses Ray for multi-node distribution, vLLM for accelerated sample generation, and DeepSpeed ZeRO-3 for memory efficiency.

Does this RLHF framework support DPO and RLOO algorithms?▼

Yes, this RLHF framework fully supports DPO and RLOO algorithms alongside PPO and GRPO. These multiple training paradigms allow you to choose the specific reinforcement learning approach that fits your model alignment needs.

What do I need to install to start RLHF training with vLLM support?▼

To start RLHF training with vLLM support, you need to install the framework by running `pip install openrlhf[vllm]`. This setup integrates vLLM to accelerate sample generation during the reinforcement learning process.

Why should I use vLLM and Ray for large language model training?▼

You should use vLLM and Ray for large language model training because they provide high-performance distributed training and accelerated inference. This combination significantly speeds up sample generation and manages resources across multiple nodes efficiently.

How does DeepSpeed ZeRO-3 help with RLHF training?▼

DeepSpeed ZeRO-3 helps with RLHF training by optimizing memory efficiency during the reinforcement learning process. It allows large 7B-70B+ parameter models to be trained with gradient checkpointing without exhausting GPU memory.

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