OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

By Jian Hu, Xibin Wu, Weixun Wang, Songlin Jiang, Dehao Zhang, Yu Cao, OpenLLMAI Team, Netease Fuxi, AI Lab, Alibaba Group. arXiv.org, 2024

349 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9deda73da446cbb21550abaa62b41f5a3acd92fc

Citations in the corpus

• Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model (2025, unknown): “Specifically, we curate our dataset through a comprehensive collection and cleaning process.”
• TÜLU 3: Pushing Frontiers in Open Language Model Post-Training (2024, unknown): “We also note that multilingual post-training can make use of different techniques to monolingual post-training – for example, cross-lingual alignment [Wu et al., 2024] or careful data balancing strategies [Li et al., 2024b].”