Distilling the Shape of Uncertainty: What On-Policy Distillation Learns and How to Predict It
arXiv:2610.39020v1 Announce Type: cross Abstract: On-policy distillation (OPD) is increasingly adopted in the post-training of large language models. However, a stronger teacher may fail to improve the student. Prior work attributes this to a lack of new knowledge, but what that knowledge is remains unclear. We show that OPD learns not only \textit{how to branch}, i.e., which candidate tokens to consider, but also \textit{where to branch}, i.e., where to keep reasoning paths open. The existing…