Rethinking Information Structures in RLHF: Reward Generalization from a Graph Theory Perspective

no code implementations • 15 Feb 2024 • Tianyi Qiu, Fanzhi Zeng, Jiaming Ji, Dong Yan, Kaile Wang, Jiayi Zhou, Yang Han, Josef Dai, Xuehai Pan, Yaodong Yang

Then, based on this framework, we introduce the IBN to analyze generalization in the reward modeling stage of RLHF.

Language Modelling Large Language Model

AI Alignment: A Comprehensive Survey

no code implementations • 30 Oct 2023 • Jiaming Ji, Tianyi Qiu, Boyuan Chen, Borong Zhang, Hantao Lou, Kaile Wang, Yawen Duan, Zhonghao He, Jiayi Zhou, Zhaowei Zhang, Fanzhi Zeng, Kwan Yee Ng, Juntao Dai, Xuehai Pan, Aidan O'Gara, Yingshan Lei, Hua Xu, Brian Tse, Jie Fu, Stephen Mcaleer, Yaodong Yang, Yizhou Wang, Song-Chun Zhu, Yike Guo, Wen Gao

The former aims to make AI systems aligned via alignment training, while the latter aims to gain evidence about the systems' alignment and govern them appropriately to avoid exacerbating misalignment risks.

Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark

no code implementations • 19 Oct 2023 • Jiaming Ji, Borong Zhang, Jiayi Zhou, Xuehai Pan, Weidong Huang, Ruiyang Sun, Yiran Geng, Yifan Zhong, Juntao Dai, Yaodong Yang

By introducing this benchmark, we aim to facilitate the evaluation and comparison of safety performance, thus fostering the development of reinforcement learning for safer, more reliable, and responsible real-world applications.

reinforcement-learning Safe Reinforcement Learning

OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research

1 code implementation • 16 May 2023 • Jiaming Ji, Jiayi Zhou, Borong Zhang, Juntao Dai, Xuehai Pan, Ruiyang Sun, Weidong Huang, Yiran Geng, Mickel Liu, Yaodong Yang

AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns.

Philosophy reinforcement-learning +2