arXiv Machine Learning
Sep 29

Learning Where It Matters: Geometric Anchoring for Robust Preference Alignment

The paper introduces Geometric Anchor Preference Optimization (GAPO), a method that replaces the static reference policy in Direct Preference Optimization with a dynamic, geometry-aware anchor—a small adversarial perturbation of the current policy. GAPO uses this anchor to adaptively reweight preference pairs based on local sensitivity, and defines an Anchor Gap that approximates worst‑case local margin degradation. Experiments show that GAPO improves robustness to noisy supervision while matching or surpassing existing LLM alignment and reasoning benchmarks.

By Youngjae Cho, Jongsuk Kim, Ji-Hoon Kim
arXiv Machine Learning
Oct 15

Unification and Optimization of Robust Supervised Learning

arXiv:2605.28165v2 Announce Type: replace Abstract: Robust learning methods such as distributionally robust optimization, vicinal risk minimization, Mixup, and label smoothing address different sourc...

By Jonas Hanselle, Valentin Margraf, Clemens Damke, Eyke H\"ullermeier
arXiv AI
Oct 2

Robust Nash Alignment under Preference Uncertainty

Robust Nash Alignment introduces a game-theoretic framework that seeks a policy with a high worst-case win rate against both an adversarial competitor and any preference kernel within an ambiguity set around a nominal preference. The authors propose a four-player primal-dual proxy game and an optimistic mirror descent-ascent algorithm to efficiently optimize this robust objective, proving convergence guarantees and demonstrating improved performance in controlled tabular games and LLM alignment experiments.

By Shihab Ahmed, Debamita Ghosh, David Tang, Yudan Wang, Alvaro Velasquez, Yue Wang