Inference-Time Amplification of Weak Reasoning Models

Excerpt

arXiv:2605.14163v2 Announce Type: replace Abstract: How much of the capability of a reasoning model exposed by repeated sampling can an imperfect selector recover? We study this problem as {\em inference-time amplification}, separating {\em coverage}---whether a useful candidate is generated---from {\em identifiability}---whether it can be recognized among the alternatives. We show that better coverage alone does not guarantee better selection, while even imperfect local selection signals can be