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[2602.19143] Incremental Learning of Sparse Attention Patterns in Transformers
Summary
This paper explores how transformers learn through incremental acquisition of sparse attention patterns, revealing shifts in learning dynamics and implications for natural language processing.
Why It Matters
Understanding how transformers adaptively learn and generalize can enhance the development of more efficient AI models, particularly in natural language processing and algorithmic reasoning. This research provides a theoretical foundation for improving model training and performance.
Key Takeaways
- Transformers learn incrementally by integrating information from past positions.
- Learning dynamics shift from competitive to cooperative among attention heads.
- Early stopping can serve as a regularizer, promoting simpler hypothesis classes.
- The study provides insights into the generalization capabilities of transformers.
- Theoretical models used reveal the complexity progression in transformer learning.
Computer Science > Machine Learning arXiv:2602.19143 (cs) [Submitted on 22 Feb 2026] Title:Incremental Learning of Sparse Attention Patterns in Transformers Authors:Oğuz Kaan Yüksel, Rodrigo Alvarez Lucendo, Nicolas Flammarion View a PDF of the paper titled Incremental Learning of Sparse Attention Patterns in Transformers, by O\u{g}uz Kaan Y\"uksel and 2 other authors View PDF HTML (experimental) Abstract:This paper introduces a high-order Markov chain task to investigate how transformers learn to integrate information from multiple past positions with varying statistical significance. We demonstrate that transformers learn this task incrementally: each stage is defined by the acquisition of specific information through sparse attention patterns. Notably, we identify a shift in learning dynamics from competitive, where heads converge on the most statistically dominant pattern, to cooperative, where heads specialize in distinct patterns. We model these dynamics using simplified differential equations that characterize the trajectory and prove stage-wise convergence results. Our analysis reveals that transformers ascend a complexity ladder by passing through simpler, misspecified hypothesis classes before reaching the full model class. We further show that early stopping acts as an implicit regularizer, biasing the model toward these simpler classes. These results provide a theoretical foundation for the emergence of staged learning and complex behaviors in transformers, off...