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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...
This work introduces the robust Multi-Agent Decoupling Coefficient (robust MADC) to characterize the intrinsic exploration complexity arising from strategic interactions and adversarial transition uncertainty, and establishes sublinear robust regret guarantees governed by the robust MADC rather than explicitly by the s...
Finite Bellman certificates are connected to undiscounted planning for state-dependent robust average rewards, providing theoretical understandings of finite Bellman solvability.
A constrained multi-objective post-training method for this setting was developed and validated, and it was verified that the method’s internal constraint-enforcement mechanism behaves as intended, applying negligible pressure while the requirement is easy to satisfy and increasing sharply as it becomes difficult to me...
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