The Geometry of Grokking: Norm Minimization on the Zero-Loss Manifold 事件

The Geometry of Grokking: Norm Minimization on the Zero-Loss Manifold arXiv:2511.01938v3 Announce Type: replace-cross Abstract: Grokking is a puzzling phenomenon in neural networks where full generalization occurs only after a substantial delay following the complete memorization of the training data. Previous research has linked this delayed generalization to representation learning driven by weight decay, but the precise underlying dynamics remain elusive. In this paper, we argue that post-me