Efficient Exploration for Iterative Nash Preference Optimization
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 (NLHF) addresses this limitation by modeling alignment as a preference game and seeking a Nash equilibrium. However, the learning-theoretic foundations of scalable NLHF remain limited: existing regret guarantees rely on explicit preference-model estimation and minimax oracles, whereas simpler iterative methods lack such guarantees. We study online iterative NLHF and identify exploration as a key obstacle. First, we show that standard iterative NLHF can incur an exponential dependence on the ...
| Authors | Tianlong Nan, Xiaopeng Li, Christian Kroer et al. |
|---|---|
| Primary category | cs.LG |
| Published | 31 May 2026 |
| Last updated at source | 2 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.