GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
OpenRLHF is a scalable and high-performance reinforcement learning framework from human feedback, built on Ray for distributed scheduling and vLLM for efficient inference, supporting models up to 70B+ parameters.
Good fit when
- When you need to train large models up to 70B+ parameters with efficient distributed scheduling and high-performance inference.
- If your project requires integration with HuggingFace Transformers for seamless model loading and fine-tuning.
Avoid when
- If your project does not require support for large models or distributed training, as OpenRLHF's complexity and resource requirements may be unnecessary.
- When you prefer a framework that does not rely on Ray for distributed scheduling, as OpenRLHF's architecture is tightly coupled with Ray for its distributed capabilities.
Observed Sep 18, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of Sep 18, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 18, 2026
- Security (OSV)
- 2 low (2 low)
- As of Sep 18, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install OpenRLHFHow it fits your stack(5)
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Depends on
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Similar tools
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Evidence and technical details
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Overview
OpenRLHF is a framework for reinforcement learning from human feedback, leveraging Ray for distributed scheduling and vLLM for high-performance inference. It supports large models up to 70B+ parameters and integrates with DeepSpeed for memory-efficient training.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Sep 18, 2026
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 18, 2026)
pip install openrlhf # BasicSource link
Tags
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 . H...
For agents
This page has a .md twin and JSON over the API.