skills.rest
ovachiever
ovachieverCOMMUNITY
Explore all skills by this author →
Website𝕏@erikjfritschGitHub

openrlhf-training

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

52|6|Updated Nov 24, 2025
Search Tags:#LLM training#vLLM#RLHF#distributed training#Ray#PPO#large language models
One-click install
npx skills add https://github.com/ovachiever/droid-tings --skill openrlhf-training
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/ovachiever/droid-tings/tree/main/skills/openrlhf
Command: npx skills add https://github.com/ovachiever/droid-tings --skill openrlhf-training

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openrlhf, ray, vllm, torch, transformers, deepspeed, and includes references (resource) components.

What problem does it solve?

This skill provides a high-performance, distributed framework for Reinforcement Learning from Human Feedback (RLHF), overcoming the challenges of training large language models (7B-70B+) by accelerating the process with Ray and vLLM. It makes RLHF training 2x faster than alternatives, significantly reducing compute costs and development cycles.

Core Features & Use Cases

  • Accelerated RLHF: Train large models (7B-70B+) with PPO, GRPO, RLOO, and DPO algorithms, leveraging Ray for distributed training and vLLM for inference acceleration.
  • Hybrid Engine Optimization: Efficiently share GPU resources across actor, critic, reward, and reference models to minimize idle time and maximize throughput.
  • Comprehensive Workflows: Supports the full RLHF pipeline from Supervised Fine-Tuning (SFT) to Reward Model training and final PPO/GRPO optimization, streamlining complex model development.
  • Use Case: Fine-tune a 70B parameter language model using PPO on a multi-GPU cluster, achieving state-of-the-art performance and significantly reducing training time compared to traditional methods.

Quick Start

Set up a Ray cluster and start PPO training for a Llama-3-8b model using the OpenRLHF framework.

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 on distributed GPU clusters?▼

RLHF training can be accelerated using Ray for distributed orchestration and vLLM for inference, achieving 2x speedup over traditional methods. This Skill enables end-to-end PPO, GRPO, RLOO, and DPO workflows on multi-node clusters with DeepSpeed memory optimization and Hybrid Engine GPU sharing.

Can I run PPO training for 70B+ parameter models with vLLM and Ray?▼

Yes. This Skill supports PPO, GRPO, RLOO, and DPO training for models 7B–70B+ using Ray for distributed training and vLLM for accelerated inference. It includes Hybrid Engine GPU resource sharing, DeepSpeed/ZeRO-3 optimization, checkpointing, and fault tolerance across multi-GPU clusters.

What's included in a complete RLHF pipeline: SFT, reward model, and PPO training?▼

This Skill provides a comprehensive end-to-end RLHF framework covering Supervised Fine-Tuning, Reward Model training, and PPO/GRPO/RLOO/DPO optimization. It integrates Ray, vLLM, DeepSpeed, and Transformers with Docker quick-start deployment and multi-node cluster support.

Does this support DeepSpeed ZeRO-3 and Hybrid Engine GPU sharing for memory efficiency?▼

Yes. The Skill integrates DeepSpeed/ZeRO-3 for distributed memory optimization and Hybrid Engine GPU sharing to efficiently allocate resources across actor, critic, reward, and reference models, minimizing idle time and maximizing training throughput.

What dependencies and infrastructure do I need to run distributed RLHF training?▼

Required dependencies are OpenRLHF, Ray, vLLM, PyTorch, Transformers, and DeepSpeed. You need multi-node GPU clusters, Docker for deployment, and familiarity with distributed training orchestration. The Skill handles Ray cluster setup and vLLM inference integration.

How does Hybrid Engine GPU optimization reduce training costs compared to traditional RLHF?▼

Hybrid Engine shares GPU resources across actor, critic, reward, and reference models, eliminating model-switching overhead and idle cycles. Combined with vLLM inference acceleration and DeepSpeed memory optimization, it delivers 2x faster training and significantly lower compute costs.

Related Skills

Explore More Software Engineering Skills→
tadod12tadod12

openrlhf-training

Orchestrate distributed RLHF training across Ray, vLLM, and ZeRO-3.

Community
Advanced
Clay-HHKClay-HHK
2

openrlhf-training

Run distributed RLHF training with Ray orchestration and vLLM acceleration.

Community
Advanced
ricablericable

openrlhf-training

Optimize RLHF training for large language models across distributed GPU clusters.

Community
Advanced
informatico-madridinformatico-madrid
1

openrlhf-training

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

Community
Advanced