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openrlhf-training

Train large language models with distributed RLHF workflows using Ray and vLLM.

Updated May 4, 2026
Search Tags:#ray#distributed training#ppo#vllm#dpo#rlhf#grpo
One-click install
npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill openrlhf-training-supporter09
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openrlhf-training
Source: https://github.com/Supporter09/Face_Anti_Spoofing_Biometric/tree/main/.claude/skills/openrlhf
Command: npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill openrlhf-training-supporter09

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

OpenRLHF training is a high-throughput framework for running reinforcement learning from human feedback on large language models without hand-managing distributed infrastructure.

Core Features & Use Cases

  • Distributed PPO, GRPO, RLOO, REINFORCE++, and DPO training with Ray and vLLM.
  • Hybrid GPU sharing across actor, critic, reward, reference, and inference engines to reduce idle time.
  • Custom reward functions and multi-step agent training for code, math, conversation, and other evaluation-heavy tasks.
  • Use case: coordinate a multi-node model alignment run, compare algorithm variants, and resume from checkpoints after interruptions.

Quick Start

Use this skill to design or explain an OpenRLHF training plan for a target model, dataset, and GPU setup.

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 across multi-node GPU clusters?▼

Distributed RLHF training across multi-node GPU clusters requires Ray for scheduling, vLLM for inference acceleration, and ZeRO-3 sharding. You can execute PPO, GRPO, RLOO, and DPO workflows using these components to manage large language model alignment efficiently.

Can I use custom reward functions for agent feedback during PPO or DPO training?▼

Yes, custom reward functions and multi-step agent training are supported for PPO and DPO workflows. You can implement specific evaluation logic for code, math, and conversation tasks, applying agent feedback directly within the distributed training pipeline.

What is the best way to reduce GPU idle time during large language model alignment?▼

To reduce GPU idle time during large language model alignment, use hybrid GPU sharing across actor, critic, reward, reference, and inference engines. Optional sleep-based GPU sharing further optimizes resource utilization during distributed RLHF training.

Does OpenRLHF training support resuming from checkpoints after an interruption?▼

Yes, OpenRLHF training supports resuming multi-node model alignment runs from checkpoints after interruptions. This allows you to recover progress and compare algorithm variants like PPO and GRPO without restarting the entire distributed workflow.

Do I need Ray and vLLM to scale GRPO and REINFORCE++ training?▼

Yes, Ray and vLLM are required to scale GRPO and REINFORCE++ training. Ray handles distributed scheduling across single-node or multi-node GPU clusters, while vLLM provides the inference acceleration necessary for high-throughput RLHF execution.

Why use ZeRO-3 sharding for distributed RLHF workflows?▼

ZeRO-3 sharding is used in distributed RLHF workflows to partition model states across multiple GPUs, preventing out-of-memory errors. It enables high-throughput training of large language models by optimizing memory consumption during PPO and DPO runs.

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