LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation 事件

LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation arXiv:2605.29280v1 Announce Type: cross Abstract: Knowledge distillation (KD) transfers a single scalar prediction from a large foundation model (FM) to compact vertical models (VMs), suffering from diminishing transfer ratio -- the fraction of FM improvement captured by the VM -- as a single scalar cannot convey the rich intermediate knowledge that larger FMs learn. To address this bottleneck, we propose Lo