Structure-Specific Representational Priors Causally Control the Grokking Delay

arXiv:2607.04333v1 Announce Type: new Abstract: Grokking -- generalization arriving long after training-set interpolation -- can be accelerated by structure-agnostic interventions: gradient filtering, weight-norm clamping, geometric penalties on hidden representations. Whether the delay specifically measures the time to form task-structured representations has remained an observational claim. We test it causally by injecting representational priors of varying structural content into a one-layer transformer learning modular addition: a supervised-contrastive auxiliary loss whose positives encod

Source: arXiv cs.LG — read the full report at the original publisher.

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