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DoanNgocCuong
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openrlhf-training

Train large language models with PPO, GRPO, RLOO, and DPO algorithms using Ray and vLLM.

Updated Aug 23, 2026
Search Tags:#fine-tuning#distributed systems#ray#ppo#vllm#rlhf#llm training
One-click install
npx skills add https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026 --skill openrlhf-training-doanngoccuong
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026/tree/main/.claude/skills/openrlhf
Command: npx skills add https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026 --skill openrlhf-training-doanngoccuong

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 complex and resource-intensive process of Reinforcement Learning from Human Feedback (RLHF) for large language models, making advanced model alignment more accessible.

Core Features & Use Cases

  • High-Performance RLHF: Supports PPO, GRPO, RLOO, DPO algorithms with Ray and vLLM acceleration.
  • Large Model Training: Optimized for models from 7B to 70B+ parameters.
  • Distributed Architecture: Built on Ray for efficient multi-node, multi-GPU training.
  • Use Case: Fine-tune a large language model like Llama-3-8B using PPO to align its responses with human preferences, significantly improving its helpfulness and safety.

Quick Start

Use the openrlhf-training skill to start PPO training for 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 accelerate RLHF training for large language models using Ray and vLLM?▼

Accelerate RLHF training by utilizing a high-performance framework built on Ray and vLLM. This approach supports distributed multi-node training across GPU clusters, optimizing generation and training phases for models from 7B to 70B+ parameters.

Can I use PPO and DPO algorithms for fine-tuning a 70B parameter model?▼

Yes, you can use PPO, DPO, GRPO, and RLOO algorithms for fine-tuning a 70B parameter model. The framework supports large model alignment by leveraging ZeRO-3, gradient checkpointing, and hybrid engine GPU sharing to manage massive parameter scales.

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

Distributed RLHF training across multi-node GPU clusters is best handled through Ray. Ray orchestrates the distributed architecture, while vLLM accelerates generation, enabling efficient resource sharing and scalable training for large language models.

Does this framework support DeepSpeed ZeRO-3 and gradient checkpointing for LLM training?▼

Yes, the framework supports DeepSpeed ZeRO-3 and gradient checkpointing for LLM training. These advanced features are integrated to optimize memory usage and enable efficient training of large models up to 70B+ parameters.

How do I start PPO training for a Llama-3-8B model on 8 GPUs?▼

Start PPO training for a Llama-3-8B model on 8 GPUs by invoking the framework's quick start command. This initiates the distributed training process to align model responses with human preferences using the specified PPO algorithm.

Why use hybrid engine GPU sharing for RLHF instead of standard distributed training?▼

Hybrid engine GPU sharing optimizes RLHF by dynamically allocating resources between generation and training phases. Unlike standard distributed training, it maximizes hardware utilization, reducing bottlenecks when processing large 7B to 70B+ parameter models.

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