skills.rest
tianhao909
tianhao909COMMUNITY
Explore all skills by this author →
GitHub

openrlhf-training

Train LLMs with PPO, GRPO, RLOO, and DPO on Ray clusters.

1|Updated Feb 21, 2026
Search Tags:#ray#distributed training#large language models#ppo#vllm#rlhf#deepseed
One-click install
npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill openrlhf-training-tianhao909
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/tianhao909/AI-Research-SKILLs-cn/tree/main/06-post-training/openrlhf
Command: npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill openrlhf-training-tianhao909

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 process of training large language models using Reinforcement Learning from Human Feedback (RLHF), offering significant speed and efficiency improvements over traditional methods.

Core Features & Use Cases

  • High-Performance RLHF: Supports PPO, GRPO, RLOO, and DPO algorithms for fine-tuning large models (7B-70B+).
  • Distributed Training: Leverages Ray for multi-node, multi-GPU distributed training.
  • Inference Acceleration: Integrates with vLLM for faster sample generation during training.
  • Memory Efficiency: Utilizes DeepSpeed ZeRO-3 and Hybrid Engine for optimized GPU memory usage.
  • Use Case: Fine-tune a Llama-3 8B model using PPO with a custom reward model, distributing the workload across 8 GPUs on a Ray cluster for faster convergence.

Quick Start

Use the openrlhf-training skill to start PPO training with a Llama-3 8B model using 8 GPUs.

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 using Ray and vLLM?▼

Distributed RLHF training for large language models is executed across multi-node Ray clusters, utilizing vLLM to accelerate sample generation and DeepSpeed ZeRO-3 for optimized GPU memory during the process.

Can I use PPO and DPO algorithms to fine-tune a 70B model on multiple GPUs?▼

Yes, PPO and DPO algorithms, alongside GRPO and RLOO, are supported for fine-tuning large 7B to 70B+ models across multi-node multi-GPU Ray clusters to achieve faster convergence.

What is the best way to optimize GPU memory usage during PPO training?▼

To optimize GPU memory usage during PPO training, the system integrates DeepSpeed ZeRO-3 and a Hybrid Engine, ensuring efficient GPU resource utilization for large model workloads.

Does vLLM work with Ray clusters for generating RLHF training samples?▼

vLLM works directly with Ray clusters to provide inference acceleration, significantly speeding up sample generation required during the RLHF training loop.

Do I need DeepSpeed to run reinforcement learning from human feedback?▼

DeepSpeed ZeRO-3 is utilized to optimize GPU memory usage and enable efficient resource utilization during reinforcement learning from human feedback, making it essential for large-scale training.

Why use a Hybrid Engine for large language model fine-tuning?▼

A Hybrid Engine is used during large language model fine-tuning to optimize GPU memory usage and ensure highly efficient resource utilization across distributed multi-node environments.

Related Skills

Explore More Software Engineering Skills→
ovachieverovachiever
52

ray-train

Orchestrate distributed training across clusters with Ray Train and Ray Tune.

Community
Advanced
ovachieverovachiever
52

serving-llms-vllm

Deploy OpenAI-compatible LLM APIs with vLLM for high-throughput inference.

Community
Advanced
nirukk52nirukk52
1

e2e-testing

Automate ScreenGraph end-to-end UI validation with Playwright and .env configuration.

Community
Intermediate
jonnabiojonnabio
6

ray-train

Scale machine learning training from single GPU to multi-node clusters.

Community
Advanced