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papersSEP 12 04:00 UTC

Study Maps Scaling Laws Behind Grokking's Delayed Generalization

A new arXiv preprint examines grokking, the phenomenon where neural networks keep memorizing training data before abruptly improving on held-out data. While prior work has focused on why this delay happens, the paper targets its quantitative structure, describing scaling laws and a phase structure that predict when the shift occurs. The authors present an arXiv preprint; the abstract excerpt provided does not detail the full experimental setup or results.

arXiv Phase transitions delayed generalization grokking scaling-laws

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGQuantifying the Memorization-to-Generalization Transition: Scaling Laws and Phase Structure in Grokking ↗SEP 11 04:00 UTC

arXiv cs.AIQuantifying the Memorization-to-Generalization Transition: Scaling Laws and Phase Structure in Grokking ↗SEP 12 04:00 UTC