OpenRLHF
Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray
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Decision brief
OpenRLHF is a reinforcement learning framework designed for efficient distributed scheduling and large-scale model training up to 70B+ parameters, leveraging Ray for resource management and integrating vLLM, DeepSpeed, H
Good fit when
- When you require high-throughput sample generation with minimal idle time on limited hardware due to its hybrid engine scheduling that enables sharing of GPU resources between models and vLLM engines.
- If your project involves large-language-models training up to 70B+ parameters where efficient memory utilization (using DeepSpeed ZeRO-3) is critical for reducing the need for heavyweight frameworks.
Avoid when
- If your project does not require distributed training or large-scale model parameters (above 70B), as OpenRLHF is specifically optimized for scenarios where efficient distribution across multiple GPUs
- When your environment cannot support Ray or vLLM, as these are crucial components of the framework for scheduling and high-performance sample generation, respectively.
- Pricing:
- freemium - OpenRLHF is primarily available free of cost under the Apache-2.0 license, but users might incur costs based on their infrastructure usage for distributed training setups (e.g., cloud GPU instances).
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
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- Active (24d since push)
- As of 1w
- Provenance
- Not a fork · Organization account
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- Security (OSV)
- 1 low (1 low)
- As of 1mo
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Install
pip install OpenRLHFHow it fits your stack(5)
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Evidence and technical details
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Overview
OpenRLHF is a reinforcement learning framework that focuses on efficient distributed scheduling for large-scale model training up to 70B+ parameters, utilizing Ray for resource management, vLLM for high-throughput sample generation, DeepSpeed for memory-efficient training, and integrates with HuggingFace Transformers.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 7, 2026
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 7, 2026)
pip install openrlhf # BasicSource link
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README
Core Infrastructure Components
Ray - Distributed Scheduler and Controller
OpenRLHF leverages Ray for efficient distributed scheduling. It separates the Actor, Reward, Reference, and Critic models across different GPUs, enabling scalable training for models up to 70B+ parameters.
Hybrid Engine Scheduling: All models and vLLM engines can share GPU resources—minimizing idle time and maximizing GPU utilization. This allows running full RLHF pipelines on limited hardware.
vLLM - High-Performance Inference Engine
RLHF training spends 80% of the time on sample generation. Powered by vLLM with Auto Tensor Parallelism (AutoTP) and Pipeline Parallelism (PP), OpenRLHF delivers high-throughput, memory-efficient generation.
DeepSpeed - Memory-Efficient Training
Built on DeepSpeed ZeRO-3, deepcompile, AutoTP, and RingAttention. Enables large model training without heavyweight frameworks while working directly with HuggingFace models.
Transformers - Model Interface
Native integration with HuggingFace Transformers for seamless model loading, state management, and fine-tuning of pretrained models.
NCCL / CUDA IPC - High-Speed Communication
Efficient inter-GPU communication for distributed training and inference.
Installation
Recommended: Use Docker for hassle-free setup
---
# 1. Launch Docker container
docker run --runtime=nvidia -it --rm --shm-size="10g" --cap-add=SYS_ADMIN \
-v $PWD:/openrlhf nvcr.io/nvidia/pytorch:26.03-py3 bash
---
# 3. Install OpenRLHF (choose one)
pip install openrlhf # Basic
pip install openrlhf[vllm] # + vLLM 0.22.1 (recommended)
pip install openrlhf[vllm_latest] # + Latest vLLM
pip install openrlhf[vllm,ring,liger] # + All optimizations
Alternative: Install from source
git clone https://github.com/OpenRLHF/OpenRLHF.git
cd OpenRLHF
pip install -e .
[!TIP] We recommend vLLM 0.22.1+ for best performance. See Dockerfiles and Nvidia-Docker Install Script.
--ds.ring_attn_size 2 # Enable RingAttention (install ring_flash_attn first)
For agents
This page has a .md twin and JSON over the API.