Omnigrok: Grokking Beyond Algorithmic Data

Ziming Liu, Eric J Michaud, Max Tegmark

Keywords: grokking, loss landscape, neural dynamics, representation learning, initialization

TL;DR: We aim to understand grokking through the lens of neural loss landscapes, and show grokking can occur for various datasets beyond algorithmic datasets.

Abstract: Grokking, the unusual phenomenon for algorithmic datasets where generalization happens long after overfitting the training data, has remained elusive. We aim to understand grokking by analyzing the loss landscapes of neural networks, identifying the mismatch between training and test losses as the cause for grokking. We refer to this as the "LU mechanism" because training and test losses (against model weight norm) typically resemble "L" and "U", respectively. This simple mechanism can nicely explain many aspects of grokking: data size dependence, weight decay dependence, the emergence of representations, etc. Guided by the intuitive picture, we are able to induce grokking on tasks involving images, language and molecules, although the grokking signals are sometimes less dramatic. We attribute the dramatic nature of grokking for algorithmic datasets to representation learning.

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Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning

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