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cs.LG2026
A Lecture Note on Offline RL and IRL, Part II: Foundations of Inverse Reinforcement Learning and Dynamic Discrete Choice Models
Enoch Hyunwook Kang
In the forward reinforcement-learning problem, the reward is fixed and known; the learner is asked to find a good policy or value function. Here we turn the question around. Given…
cs.LG2026
Personalized Alignment Revisited: The Necessity and Sufficiency of User Diversity
Enoch Hyunwook Kang
Personalized alignment aims to adapt large language models to heterogeneous user preferences, yet the precise theoretical conditions for its statistical efficiency have not been fo…
cs.LG2026
Demystifying the Unreasonable Effectiveness of Greedy Alignment Methods
Enoch Hyunwook Kang
Greedy AI alignment methods based on Bradley-Terry reward-model fitting and KL-regularized Alignment are remarkably effective in both offline and online preference-learning pipelin…