Identify the sources of PatchWAM’s matched-setting advantage

Identify the factors responsible for PatchWAM’s advantage over the dual-expert control by conducting further controls under the matched training setting and by using independent training seeds.

Background

PatchWAM outperforms the matched dual-expert control in the reported sparse-window training regime, but the comparison changes several factors simultaneously, including action-specific capacity, token visibility, and the processing path. The paper’s full-data controls partially examine these factors but do not explain the performance gap observed in the matched comparison.

The authors explicitly state that additional matched-setting controls and independent training seeds are required to determine which design choices account for the observed advantage. This remains unresolved because the reported experiments use single training runs and do not isolate all relevant architectural differences.