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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…
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…
Reasonably reasoning AI agents can avoid game-theoretic failures in zero-shot, provably
Enoch Hyunwook Kang
As autonomous AI agents increasingly mediate online platform markets, a fundamental question emerges: do these markets generate stable strategic outcomes? In repeated strategic env…
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…
LLM Personas as a Substitute for Field Experiments in Method Benchmarking
Enoch Hyunwook Kang
Field experiments (A/B tests) are often the most credible benchmark for methods (algorithms) in societal systems, but their cost and latency bottleneck rapid methodological progres…