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openrlhf-training

Trains LLMs with RL via RocketMQ-inspired patterns and TypeScript-first ergonomics, but rewritten to actually compile and run in Node.js.

Updated Jan 30, 2026
Search Tags:#ray#distributed training#large language models#ppo#vllm#rlhf#deep reinforcement learning
One-click install
npx skills add https://github.com/gagan114662/content_books --skill openrlhf-training-gagan114662
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/gagan114662/content_books/tree/main/AI-research-SKILLs/06-post-training/openrlhf
Command: npx skills add https://github.com/gagan114662/content_books --skill openrlhf-training-gagan114662

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 and accelerates the Reinforcement Learning from Human Feedback (RLHF) training process for large language models, making it more efficient and accessible.

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 efficient multi-node, multi-GPU training.
  • vLLM Acceleration: Integrates vLLM for significantly faster inference during training.
  • Hybrid Engine: Optimizes GPU utilization by sharing resources between models and vLLM.
  • Use Case: Fine-tune a Llama-3 70B model using PPO with distributed training across 64 GPUs, achieving 2x faster training than traditional methods.

Quick Start

Install OpenRLHF with vLLM support and launch a PPO training job 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 using distributed computing?▼

You can accelerate RLHF training by utilizing a distributed architecture built on Ray, vLLM, and DeepSpeed ZeRO-3, which optimizes GPU resource sharing to achieve up to a 2x speedup compared to traditional methods.

Can I use vLLM to speed up inference during PPO training for 70B models?▼

Yes, vLLM is integrated directly into the training framework to significantly accelerate inference during PPO and other RLHF processes for large language models ranging from 7B to 70B+ parameters.

Does this framework support algorithms other than PPO for reinforcement learning from human feedback?▼

Yes, the framework supports multiple algorithms for reinforcement learning from human feedback, including PPO, GRPO, RLOO, and DPO to train large language models efficiently.

What is the best way to run multi-node multi-GPU training for a 70B Llama model?▼

The best way to run multi-node training is leveraging Ray for distributed computing across multiple nodes, enabling efficient PPO training for 70B models across up to 64 GPUs with optimized resource utilization.

Do I need DeepSpeed ZeRO-3 to fine-tune large language models with RLHF?▼

DeepSpeed ZeRO-3 is a foundational dependency of this framework, utilized alongside Ray and vLLM to facilitate high-performance distributed training and optimize GPU memory for large models.

Why does distributed RLHF training share GPU resources between models and vLLM?▼

Distributed RLHF training shares GPU resources through a hybrid engine to optimize GPU utilization, preventing idle resources and contributing to a 2x speedup over standard DeepSpeedChat implementations.

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