2 papers
cs.LG2026
Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning
Anthony GX-Chen, Ankit Anand, Gheorghe Comanici +7
Classical reinforcement learning (RL) typically seeks a deterministic policy that maximizes the expected sum of a scalar reward. Yet, modern applications such as language model fin…
cs.LG2026
Spectral Souping: A Unified Framework for Online Preference Alignment
Yinlam Chow, Guy Tennenholtz, Ted Yun +4
Reinforcement Learning from Human Feedback (RLHF) effectively aligns Large Language Models (LLMs) with aggregate human preferences but often fails to address the diverse and confli…