Canalization Before Generalization: Grokking as a Dynamical Probe

Abstract:For overparameterized neural networks, many solutions can fit the training data equally well while behaving very differently on unseen samples. Grokking separates training fit from visible generalization, providing a window for studying how this selection develops during training. We sweep short, fixed-duration weight-decay (WD) pulses across this plateau and measure how they shift later generalization time. Across three grokking tasks, these shifts are unordered early in the plateau but later form a stable dose ordering, with stronger WD increases leading to earlier generalization and stronger WD decreases leading to later generalization. This ordering emerges before visible generalization in all three tasks. Meanwhile, test-loss barriers between perturbed and baseline generalization checkpoints collapse toward zero while the ordered timing effects persist. We call this combination of increasingly constrained solution selection and persistent dose-ordered timing sensitivity the canalization of function selection.

Machine Learning (cs.LG)

arXiv:2608.25813 [cs.LG]

(or arXiv:2608.25813v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.25813 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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