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openrlhf-training

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

1|Updated Feb 10, 2026
Search Tags:#deep learning#distributed systems#ray#vllm#rlhf#llm training
One-click install
npx skills add https://github.com/informatico-madrid/Architect-Expert-Gap-Forge --skill openrlhf-training-informatico-madrid
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/informatico-madrid/Architect-Expert-Gap-Forge/tree/main/.github/skills/openrlhf
Command: npx skills add https://github.com/informatico-madrid/Architect-Expert-Gap-Forge --skill openrlhf-training-informatico-madrid

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, addressing challenges in distributed training, GPU utilization, and algorithm selection.

Core Features & Use Cases

  • High-Performance RLHF: Supports PPO, GRPO, RLOO, DPO training for models from 7B to 70B+ parameters.
  • Distributed Training: Leverages Ray for multi-node, multi-GPU setups with efficient resource sharing (Hybrid Engine).
  • Inference Acceleration: Integrates vLLM for significantly faster sample generation.
  • Use Case: Fine-tune a Llama-3 8B model using PPO with a custom reward model, distributing the workload across 8 GPUs for faster convergence.

Quick Start

Use the openrlhf-training skill to initiate PPO training for a Llama-3 8B model using Ray and vLLM acceleration.

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 across multiple GPUs?▼

Distributed RLHF training for large language models is executed using Ray for multi-node setups and vLLM for inference acceleration, supporting models from 7B to 70B+ parameters. DeepSpeed ZeRO-3 optimizes multi-node training efficiency.

What is the best way to fine-tune a Llama-3 8B model using PPO with a custom reward model?▼

Fine-tuning a Llama-3 8B model using PPO involves distributing the workload across multiple GPUs with Ray. The process leverages vLLM for faster sample generation and utilizes DeepSpeed ZeRO-3 optimization for efficient resource sharing.

Does RLHF training with Ray support algorithms other than PPO?▼

RLHF training with Ray supports multiple algorithms beyond PPO, including GRPO, RLOO, and DPO. These algorithms are integrated to facilitate high-performance reinforcement learning for large language models.

Can I use vLLM to accelerate sample generation during PPO training?▼

vLLM is integrated directly into the RLHF pipeline to significantly accelerate sample generation during PPO training. This combination with Ray enables efficient GPU resource sharing through a Hybrid Engine.

What are the limitations of using DeepSpeed ZeRO-3 for 70B parameter model training?▼

DeepSpeed ZeRO-3 optimization for 70B parameter models requires distributed multi-node setups to manage memory constraints. While it enables efficient training, users must provision adequate GPU resources to avoid bottlenecks.

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