PILOT: Policy-Informed Learned Optimization for Adaptive Deep Network Training 事件

PILOT: Policy-Informed Learned Optimization for Adaptive Deep Network Training arXiv:2605.24570v1 Announce Type: cross Abstract: Despite the central role of optimization in deep learning, most optimizers rely on update structures whose functional form is fixed before training begins. This static design can limit their ability to respond to changing gradient behavior across the loss landscape, where training may shift between stable, noisy, and inconsistent regimes. This study proposes PILOT (