@inproceedings{hu-etal-2025-openrlhf,
title = "{O}pen{RLHF}: A Ray-based Easy-to-use, Scalable and High-performance {RLHF} Framework",
author = "Hu, Jian and
Wu, Xibin and
Shen, Wei and
Liu, Jason Klein and
Wang, Weixun and
Jiang, Songlin and
Wang, Haoran and
Chen, Hao and
Chen, Bin and
Fang, Wenkai and
Xianyu and
Cao, Yu and
Xu, Haotian and
Liu, Yiming",
editor = {Habernal, Ivan and
Schulam, Peter and
Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-demos.48/",
pages = "656--666",
ISBN = "979-8-89176-334-0",
abstract = "Large Language Models (LLMs) fine-tuned via Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) significantly improve the alignment of human-AI values and further raise the upper bound of AI capabilities, particularly in reasoning-intensive, long-context Chain-of-Thought (long-CoT) tasks. However, existing RLHF (or RLVR) frameworks commonly face challenges such as inference bottlenecks and complexity barriers, restricting their accessibility for newcomers. To bridge this gap, we introduce \textbf{OpenRLHF}, a user-friendly, scalable, and easy-to-learn open-source RLHF framework built upon Ray, vLLM, DeepSpeed, and HuggingFace Transformers, featuring a simplified design, clear code structure, and comprehensive documentation to facilitate entry for researchers and practitioners. Experimental results show that OpenRLHF achieves superior training efficiency with speedups ranging from 1.22{\texttimes} to 1.68{\texttimes} across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation. OpenRLHF is publicly available at \url{https://github.com/OpenRLHF/OpenRLHF}, and has already been adopted by leading institutions to accelerate RLHF research and learning."
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<abstract>Large Language Models (LLMs) fine-tuned via Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) significantly improve the alignment of human-AI values and further raise the upper bound of AI capabilities, particularly in reasoning-intensive, long-context Chain-of-Thought (long-CoT) tasks. However, existing RLHF (or RLVR) frameworks commonly face challenges such as inference bottlenecks and complexity barriers, restricting their accessibility for newcomers. To bridge this gap, we introduce OpenRLHF, a user-friendly, scalable, and easy-to-learn open-source RLHF framework built upon Ray, vLLM, DeepSpeed, and HuggingFace Transformers, featuring a simplified design, clear code structure, and comprehensive documentation to facilitate entry for researchers and practitioners. Experimental results show that OpenRLHF achieves superior training efficiency with speedups ranging from 1.22× to 1.68× across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation. OpenRLHF is publicly available at https://github.com/OpenRLHF/OpenRLHF, and has already been adopted by leading institutions to accelerate RLHF research and learning.</abstract>
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%0 Conference Proceedings
%T OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework
%A Hu, Jian
%A Wu, Xibin
%A Shen, Wei
%A Liu, Jason Klein
%A Wang, Weixun
%A Jiang, Songlin
%A Wang, Haoran
%A Chen, Hao
%A Chen, Bin
%A Fang, Wenkai
%A Cao, Yu
%A Xu, Haotian
%A Liu, Yiming
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%A Xianyu
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F hu-etal-2025-openrlhf
%X Large Language Models (LLMs) fine-tuned via Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) significantly improve the alignment of human-AI values and further raise the upper bound of AI capabilities, particularly in reasoning-intensive, long-context Chain-of-Thought (long-CoT) tasks. However, existing RLHF (or RLVR) frameworks commonly face challenges such as inference bottlenecks and complexity barriers, restricting their accessibility for newcomers. To bridge this gap, we introduce OpenRLHF, a user-friendly, scalable, and easy-to-learn open-source RLHF framework built upon Ray, vLLM, DeepSpeed, and HuggingFace Transformers, featuring a simplified design, clear code structure, and comprehensive documentation to facilitate entry for researchers and practitioners. Experimental results show that OpenRLHF achieves superior training efficiency with speedups ranging from 1.22× to 1.68× across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation. OpenRLHF is publicly available at https://github.com/OpenRLHF/OpenRLHF, and has already been adopted by leading institutions to accelerate RLHF research and learning.
%U https://aclanthology.org/2025.emnlp-demos.48/
%P 656-666
Markdown (Informal)
[OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework](https://aclanthology.org/2025.emnlp-demos.48/) (Hu et al., EMNLP 2025)
ACL
- Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, Xianyu, Yu Cao, Haotian Xu, and Yiming Liu. 2025. OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 656–666, Suzhou, China. Association for Computational Linguistics.