The study explores how gradient flow implicitly regularizes the nuclear norm of attention weights in one-layer softmax models. It contrasts with prior results on Frobenius norm regularization, showing convergence to optimal solutions for binary classification tasks. The alignment property simplifies dynamics, ensuring global optimality and minimal loss convergence. Assumptions on data separability and initialization conditions drive the analysis, revealing insights into training dynamics of attention-based models.
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