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Study reveals grokking in MLPs sharpens pre-existing algorithms

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv investigates the phenomenon of 'grokking' in neural networks, specifically focusing on ReLU MLPs trained on modular arithmetic. Researchers found that instead of sinusoidal weight distributions, these networks develop near-binary square wave input weights and output weights that satisfy a specific phase-sum relation. By constructing an idealized MLP using these extracted parameters, the study demonstrated that the network substantially encodes the correct algorithm during memorization, with grokking serving to refine this algorithm by binarizing input weights and aligning output weights, rather than discovering it anew. AI

IMPACT Provides insight into how neural networks learn and generalize, potentially informing future model development.

RANK_REASON The cluster contains a single academic paper detailing a mechanistic study of a machine learning phenomenon. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Study reveals grokking in MLPs sharpens pre-existing algorithms

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The cluster contains a single academic paper detailing a mechanistic study of a machine learning phenomenon. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Anand Swaroop ·

    Latent Algorithmic Structure Precedes Grokking: A Mechanistic Study of ReLU MLPs on Modular Arithmetic

    arXiv:2603.23784v1 Announce Type: cross Abstract: Grokking-the phenomenon where validation accuracy of neural networks on modular addition of two integers rises long after training data has been memorized-has been characterized in previous works as producing sinusoidal input weig…

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