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Training & fine-tuning

OpenRLHF

Combines distributed RLHF components with extensible single-turn and multi-turn agent training workflows.

Overview

Research summary

OpenRLHF provides workflows for supervised adaptation, reward modeling and reinforcement learning from feedback. It uses Ray to coordinate distributed components and integrates generation engines such as vLLM with training backends. Its documented algorithms and examples cover preference-oriented training, custom rewards and agent interactions across multiple turns. Developers can adapt these components to their model and environment rather than writing the full distributed feedback loop. Backend capabilities and supported algorithms should be checked against the selected release and recipe.

Repository summary

Stars
10,063
Open issues
Unavailable
Last push
2026-09-17
Commits, 90 days
Unavailable
Repository activity
Not scored
Version
Unavailable

Recorded catalogue figures. View repository data and provenance →

Classification

Pricing & services

Paid services unknown

Whether the provider offers paid products or services has not been established.

Licence scope

Implementation

Recorded implementation details and interfaces for OpenRLHF.

Implementation details

Python library and supporting tools

Languages
Python
Repository type
source

Recorded interfaces and capabilities

Licence scope

Repository

Repository snapshots, release information and recorded maintenance signals.

Repository snapshot

OpenRLHF/OpenRLHF ↗

Stars
10,063
Open issues
Unavailable
Last push
2026-09-17
Commits, 90 days
Unavailable
Repository activity
Not scored
Archived
Not recorded

Repository activity is a snapshot, not a quality or popularity ranking. It combines recent-push freshness (50%), 90-day commits (30%) and issue pressure (20%).

Maintenance and provenance

Catalogue snapshot
2026-10-06
Stars source
Recorded fallback
Last push source
Recorded fallback

Documentation

Recorded references and research provenance for this entry.

Recorded sources 5

Research metadata

Research date
2026-10-02
Catalogue snapshot
2026-10-06

Community & social 1 channels

Communities