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NeurIPS 2023

Transformers learn through gradual rank increase

Conference Paper Main Conference Track Artificial Intelligence ยท Machine Learning

Abstract

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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Keywords

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Context

Venue
Annual Conference on Neural Information Processing Systems
Archive span
1987-2025
Indexed papers
30776
Paper id
694714768366996673
v2026.09.13