Differentiable Attention Sparsity via Structured $D$-Gating
Track: long paper (up to 4 pages)
Keywords: Sparsity, Attention, Non-Smoothness, Optimization
Abstract: A core component of modern large language models is the attention mechanism, but its immense parameter count necessitates structured sparsity for resource-efficient optimization and inference. Traditional sparsity penalties, such as the group lasso, are non-smooth and thus incompatible with standard stochastic gradient descent methods. To address this, we propose a deep gating mechanism that reformulates the structured sparsity penalty into a fully differentiable optimization problem, allowing effective and principled norm-based group sparsification without requiring specialized non-smooth optimizers. Our theoretical analysis and empirical results demonstrate that this approach enables structured sparsity with simple stochastic gradient descent or variants while maintaining predictive performance.
Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.
Presenter: ~Chris_Kolb1
Format: Yes, the presenting author will definitely attend in person because they attending ICLR for other complementary reasons.
Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.
Submission Number: 48
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