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openrlhf-training

Train large language models with RLHF using Ray and vLLM.

3|Updated Feb 21, 2026
Search Tags:#fine-tuning#ray#distributed training#large language models#ppo#vllm#rlhf
One-click install
npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill openrlhf-training-ihatesea69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/06-post-training/openrlhf
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill openrlhf-training-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openrlhf, ray, vllm, torch, transformers, deepspeed, and includes scripts (resource) and 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 it faster and more efficient.

Core Features & Use Cases

  • High-Performance Training: Utilizes Ray and vLLM for distributed training and accelerated inference, achieving up to 2x speed improvements over alternatives.
  • Multiple RL Algorithms: Supports PPO, GRPO, RLOO, and DPO, offering flexibility for different training needs and resource constraints.
  • Use Case: Fine-tune a 70B parameter model using PPO with distributed GPUs, leveraging vLLM for efficient generation and DeepSpeed ZeRO-3 for memory optimization.

Quick Start

Use the openrlhf-training skill to start PPO training with 8 GPUs 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 use Ray and vLLM for distributed RLHF training?▼

Distributed RLHF training uses Ray for cluster management and vLLM for inference acceleration, achieving up to 2x speed improvements over alternative approaches for large language models.

What algorithms are supported for fine-tuning large language models with human feedback?▼

Supported algorithms for fine-tuning large language models with human feedback include PPO, GRPO, RLOO, and DPO, offering flexibility for different training needs and resource constraints.

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

Yes, you can fine-tune a 70B parameter model using PPO with distributed GPUs, leveraging vLLM for efficient generation and DeepSpeed ZeRO-3 for memory optimization to handle large-scale training.

Does RLHF training work with DeepSpeed ZeRO-3 and gradient checkpointing?▼

RLHF training works with DeepSpeed ZeRO-3 and gradient checkpointing to provide memory optimization necessary for handling large language models ranging from 7B to 70B+ parameters.

What is the best way to accelerate Reinforcement Learning from Human Feedback for large language models?▼

Accelerating Reinforcement Learning from Human Feedback for large language models is best achieved using a high-performance architecture combining Ray for distributed cluster management and vLLM for inference acceleration.

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