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openrlhf-training

Train LLMs with PPO, GRPO, RLOO, and DPO using Ray and vLLM.

Updated Jan 12, 2026
Search Tags:#ray#distributed training#ppo#vllm#rlhf#llm training#deep reinforcement learning
One-click install
npx skills add https://github.com/MesferAli/XCircle --skill openrlhf-training-mesferali
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/MesferAli/XCircle/tree/main/.claude/skills/openrlhf
Command: npx skills add https://github.com/MesferAli/XCircle --skill openrlhf-training-mesferali

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 (LLMs) using Reinforcement Learning from Human Feedback (RLHF), offering significant speed and efficiency gains.

Core Features & Use Cases

  • High-Performance RLHF: Supports PPO, GRPO, RLOO, and DPO training for models from 7B to 70B+ parameters.
  • Distributed Training: Leverages Ray for multi-node, multi-GPU training.
  • vLLM Acceleration: Integrates vLLM for faster inference during sample generation.
  • Use Case: Fine-tune a Llama-3 8B model using PPO with a custom reward model to align its responses with desired safety and helpfulness criteria, achieving faster convergence than standard methods.

Quick Start

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

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 a 7B-70B+ LLM?▼

Distributed RLHF training for large language models is handled using Ray and vLLM for acceleration. It supports PPO, GRPO, RLOO, and DPO algorithms for efficient training.

Can I use vLLM for faster inference during LLM training?▼

vLLM is integrated specifically to accelerate inference during sample generation in RLHF workflows. This significantly speeds up the training loop for large language models.

Does this Skill support PPO and DPO for fine-tuning models like Llama-3?▼

Yes, PPO and DPO are fully supported alongside GRPO and RLOO for fine-tuning models like Llama-3 8B. It leverages Ray, vLLM, and ZeRO-3 for efficient distributed training.

What is the best way to set up multi-node multi-GPU training for RLHF?▼

Multi-node, multi-GPU training for RLHF is streamlined using Ray for resource management. This architecture enables high-performance training of 7B to 70B+ parameter models.

Do I need Ray and DeepSpeed to accelerate PPO training?▼

Ray is required for distributed training orchestration, while DeepSpeed ZeRO-3 is used for memory optimization. Both are integrated to accelerate PPO and other RLHF algorithms.

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