4 citations · 6 across the 8 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cs.LG2024★ 1 cited
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning
Yuheng Zhang, Dian Yu, Baolin Peng +6
Reinforcement Learning with Human Feedback (RLHF) has achieved great success in aligning large language models (LLMs) with human preferences. Prevalent RLHF approaches are reward-b…
cs.LG2024★ 4 cited
RLHF Workflow: From Reward Modeling to Online RLHF
Hanze Dong, Wei Xiong, Bo Pang +7
We present the workflow of Online Iterative Reinforcement Learning from Human Feedback (RLHF) in this technical report, which is widely reported to outperform its offline counterpa…
cs.LG2024
Online Iterative Reinforcement Learning from Human Feedback with General Preference Model
Chenlu Ye, Wei Xiong, Yuheng Zhang +3
We investigate Reinforcement Learning from Human Feedback (RLHF) in the context of a general preference oracle. In particular, we do not assume the existence of a reward function a…