Noise‑Driven Escape Theory Illuminates Grokking in Deep Nets
The paper shows that grokking results from stochastic gradient descent noise pushing a network out of metastable states created by low L2 regularization. This first‑order phase transition explains why generalization can appear abruptly after prolonged overfitting, and suggests ways to speed up learning.