Shuo Li Liu - Coherence in RLHF Preference Data Shuo Li Liu - Coherence in RLHF Preference Data

RLHF usually learn from pairwise comparisons, often through Bradley-Terry-style models. I will discuss what coherence requirements, such as Weak Stochastic Transitivity and the Weak Axiom of Revealed Preference, mean for preference trained AI systems.

Shuo Li Liu is a PhD student in Economics at Princeton University. His work connects axiomatic decision theory and AI alignment, with current projects on stochastic choice, preference learning, and the foundations of RLHF evaluation.

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