Training Dynamics and Decision Stability in Multi-Objective Reinforcement Learning via Spectral Sensitivity Analysis
David Tang, Yue Wang
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Robust Nash Alignment under Preference Uncertainty
Preference-based alignment methods typically optimize against a single preference model, and can therefore be brittle when pairwise preferences are uncertain: noisy, heterogeneous, or shift after deployment. To address these issues, we propose Robust Nash Alignment, a game-theoretic framework for alignment to uncertain...
Shihab Ahmed, Debamita Ghosh, David Tang et al.
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