Tian-Yi Lin

This paper proposes and analyzes Comparison-based Preference Optimization (ComPO), a zeroth-order alignment method based on comparison oracles, and establishes a convergence guarantee for its basic offline scheme under smoothness, gradient sparsity, and compatibility between the oracle and a latent objective.

Global AI aggregators can actually *worsen* learning compared to specialized, local ones, especially when they update too quickly.