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New framework uses renormalization group theory to describe neural network grokking

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework using scaling and renormalization group (RG) theories to describe the phenomenon of grokking in deep neural networks. Grokking is characterized by a delayed transition from memorization to generalization. The study, focusing on modular addition with a two-layer neural network, established an order parameter and fluctuation proxies that align with training epoch and weight decay as control coordinates. By constructing an exact coupling matrix and using its Fiedler vector to define an effective geometry, the researchers derived scaling exponents and critical exponents that closely matched empirical estimates. AI

IMPACT Provides a theoretical framework for understanding generalization in neural networks, potentially leading to more robust and efficient model training.

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework for understanding neural network dynamics. [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 →

New framework uses renormalization group theory to describe neural network grokking

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The cluster contains an academic paper detailing a new theoretical framework for understanding neural network dynamics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Min Chen, Junqi Wang, Zeguan Wu, Wenhao Huang, Junyu Liu ·

    Scaling and Renormalization in Neural Networks: An Effective Renormalization-Group Description of Grokking

    arXiv:2610.36113v1 Announce Type: cross Abstract: Grokking is a delayed transition from memorization to generalization in deep neural networks (NNs). We examine whether scaling and renormalization group (RG) theories can describe this dynamics, establishing a quantitative physica…

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