Relationship between identifier structure and convergence dynamics in SEATER

Investigate the relationship between the structure of the balanced k-ary hierarchical item identifiers used by the SEATER generative retrieval model and the model’s training convergence dynamics, to ascertain how the identifier structure affects convergence behavior.

Background

SEATER organizes items into balanced k-ary trees to form hierarchical identifiers that guide generative retrieval. The original tree is built with capacity-constrained k-means; this paper proposes faster greedy and hybrid alternatives for constructing the tree.

During experiments, the authors observed that overall training time decreased even when excluding tree construction time, which they hypothesize may be due to faster model convergence driven by differences in identifier structure. They explicitly leave investigation of this relationship for future work, highlighting an unresolved question about how tree-induced identifier structures influence convergence dynamics.