To Grok Grokking: Provable Grokking in Ridge Regression
Proves delayed generalization in ridge regression. Analysis of gradient descent with weight decay connects the delay to training hyperparameters and examines how tuning can amplify or remove grokking.
Overview
Proves delayed generalization in ridge regression. Analysis of gradient descent with weight decay connects the delay to training hyperparameters and examines how tuning can amplify or remove grokking.
Proves delayed generalization in ridge regression. Analysis of gradient descent with weight decay connects the delay to training hyperparameters and examines how tuning can amplify or remove grokking.
When reading the original paper, identify its problem, inputs, outputs, and methodological assumptions.
Proves delayed generalization in ridge regression. Analysis of gradient descent with weight decay connects the delay to training hyperparameters and examines how tuning can amplify or remove grokking.
Check the baselines, datasets, evaluation protocol, and ablations in the original paper. This directory overview does not provide an independent evaluation or experimental results table.
References
ID: icml-2026-grokking-ridge-regression · Last verified:
