Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning.

arXiv:2610.17735v1 Announce Type: cross Abstract: Multi-sample reasoning measures candidate coverage, the probability that at least one of $K$ candidates is correct. However, minimizing the average loss of individual attempts does not directly optimize candida