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cs.AI2026
Robust Nash Alignment under Preference Uncertainty
Shihab Ahmed, Debamita Ghosh, David Tang +3
Preference-based alignment methods typically optimize against a single preference model, and can therefore be brittle when pairwise preferences are uncertain: noisy, heterogeneous,…
cs.AI2023★ 1 cited
Automaton-Guided Curriculum Generation for Reinforcement Learning Agents
Yash Shukla, Abhishek Kulkarni, Robert Wright +2
Despite advances in Reinforcement Learning, many sequential decision making tasks remain prohibitively expensive and impractical to learn. Recently, approaches that automatically g…