arXiv:2606.18465v1 Announce Type: cross Abstract: Grokking, the delayed jump from memorization to generalization, is usually tied to the weight norm: a smaller norm generalizes sooner. We ask what the norm actually controls. Holding the weight norm fixed by clamping and varying only an output temperature, we slide the grokking delay across its entire norm-induced range under cross-entropy; matching the effective logit scale back to baseline recovers about 85% of the delay at two moduli. Across a grid of norms and temperatures the delay collapses onto the logit scale alone (R2 = 0.97), with the
Ongoing research in AI interpretability and generalization is continuously revealing deeper mechanisms behind model performance, making this a natural progression in understanding current AI phenomena like grokking.
A strategic reader should care because deeper understanding of AI generalization leads to more robust and efficient model development, impacting AI safety, deployment, and performance predictability.
This research refines our understanding of how AI models achieve generalization, suggesting that logit scale, rather than just weight norm, is a primary control factor in the 'grokking' phenomenon.
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Refined understanding of AI generalization improves model design and training protocols.
More predictable and less 'brittle' AI systems emerge, reducing deployment risks and increasing adoption in critical applications.
The ability to reliably control generalization and memorization leads to more efficient use of computational resources and faster R&D cycles for novel AI architectures.
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