Efficient Exploration for Iterative Nash Preference Optimization.

arXiv:2606.01382v3 Announce Type: replace-cross Abstract: Preference alignment is central to improving large language models (LLMs), but reward-based formulations can be restrictive when human preferences are non-transitive. Nash learning from human feedback (