Structure-Specific Representational Priors Causally Control the Grokking Delay
Grokking -- generalization long after training-set interpolation -- has been accelerated by structure-agnostic interventions (gradient filtering, weight-norm clamping, geometric penalties). Whether the delay specifically measures the time to form task-structured representations has remained observational. We test it causally by injecting representational priors of varying content into a one-layer transformer learning modular addition, via a supervised-contrastive loss whose positives encode (i) the task's true structure ($(a+b) \bmod p$), (ii) a coherent-but-wrong sibling ($(a-b) \bmod p$), or (iii) a random partition -- all with identical loss form, strength, class sizes, and geometry. W...
| Authors | Gunner Levi Howe |
|---|---|
| Primary category | cs.LG |
| Published | 5 July 2026 |
| Last updated at source | 14 July 2026 |
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