To Grok Grokking: Provable Grokking in Ridge Regression

Jul 17, 2026

Researchers provide the first rigorous, quantitative bounds on 'grokking time'—the delayed generalization phenomenon—in overparameterized linear models trained with weight decay. They prove that grokking can be amplified or eliminated through hyperparameter tuning and show that their theoretical results extend empirically to some non-linear neural networks. This work clarifies that grokking is a consequence of specific training conditions, not an inherent failure of deep learning architectures.

Why it matters: This study offers a principled understanding of grokking and practical guidance for controlling it, advancing both theory and practice in machine learning.

Full story at: arXiv Statistical ML ↗