skills.rest
jonnabio
jonnabioCOMMUNITY
Explore all skills by this author →
𝕏@jonnabioGitHub

openrlhf-training

Train large language models with RLHF algorithms using distributed GPU resources.

6|3|Updated Jan 29, 2026
Search Tags:#RLHF#distributed training#large language models#acceleration#GPU optimization
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill openrlhf-training-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/openrlhf
Command: npx skills add https://github.com/jonnabio/ace-framework --skill openrlhf-training-jonnabio

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenges of large-scale Reinforcement Learning with Human Feedback (RLHF) training by providing a high-performance framework that accelerates training and simplifies distributed execution.

Core Features & Use Cases

  • Distributed Training: Achieve efficient training for large models with distributed architecture and GPU resource sharing.
  • RLHF Framework: Offers the flexibility to use various algorithms like PPO, GRPO, RLOO, and DPO for training large language models.
  • Hybrid Engine: Implements a GPU-sharing system that optimizes GPU utilization and minimizes idle time during training.
  • Use Case: Suppose you are working with a 70B parameter language model and require efficient training with vLLM inference acceleration. This Skill can enable you to train your model in a distributed environment, leveraging the Hybrid Engine for improved performance.

Quick Start

Use the openrlhf-training skill to initiate distributed PPO training for a 70B language model.

Frequently Asked Questions about openrlhf-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I accelerate distributed RLHF training for large language models?▼

Accelerate distributed RLHF training for large language models by leveraging vLLM inference acceleration and a hybrid engine for GPU resource sharing, minimizing idle time during PPO, GRPO, RLOO, and DPO execution.

Does RLHF training with vLLM support 70B parameter models?▼

RLHF training with vLLM supports 70B parameter models by utilizing distributed execution and ZeRO-3 optimization, enabling efficient GPU resource sharing and inference acceleration for large-scale language models.

What is the best way to optimize GPU utilization during PPO and DPO training?▼

Optimize GPU utilization during PPO and DPO training by implementing a hybrid engine that shares GPU resources, reducing idle time and maximizing efficiency across distributed training nodes using Ray and DeepSpeed.

Do I need Ray and DeepSpeed to run distributed RLHF algorithms?▼

Ray and DeepSpeed are required to run distributed RLHF algorithms efficiently. Ray manages distributed execution while DeepSpeed provides ZeRO-3 optimization, ensuring effective GPU resource sharing during large language model training.

Can I use GRPO and RLOO algorithms for large-scale reinforcement learning?▼

GRPO and RLOO algorithms are fully supported for large-scale reinforcement learning alongside PPO and DPO, offering flexibility in training large language models within a distributed architecture using vLLM acceleration.

Why does large-scale RLHF training require ZeRO-3 optimization?▼

Large-scale RLHF training requires ZeRO-3 optimization to efficiently partition model states across multiple GPUs, preventing memory bottlenecks and enabling the distributed training of 70B parameter models with vLLM inference acceleration.

Related Skills

Explore More Software Engineering Skills→
ovachieverovachiever
52

openrlhf-training

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

Community
Advanced
ovachieverovachiever
52

serving-llms-vllm

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

Community
Advanced
Ced3-hanCed3-han

openrlhf-training

Run distributed RLHF training for large language models with Ray and vLLM.

Community
Advanced
informatico-madridinformatico-madrid
1

openrlhf-training

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

Community
Advanced