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Transformers learn through gradual rank increase

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arxiv 2306.07042 v2 pith:QFPB6HZM submitted 2023-06-12 cs.LG

Enric Boix-Adsera , Etai Littwin , Emmanuel Abbe , Samy Bengio , Joshua Susskind This is my paper · ORCID
classification cs.LG
keywords assumptionsranksimplifyingtransformersdiagonaldifferencedynamicsexperiments
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We identify incremental learning dynamics in transformers, where the difference between trained and initial weights progressively increases in rank. We rigorously prove this occurs under the simplifying assumptions of diagonal weight matrices and small initialization. Our experiments support the theory and also show that phenomenon can occur in practice without the simplifying assumptions.

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Cited by 1 Pith paper

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  1. Learning In-context n-grams with Transformers: Sub-n-grams Are Near-stationary Points

    cs.LG 2025-08 reject novelty 7.0 of 10

    Sub-n-gram estimators are near-stationary points of the population cross-entropy loss for in-context n-gram learning, offering a theoretical explanation for stage-wise training plateaus.

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