From Sparsity to Simplicity: Enabling Simpler Sequential Replacements via Sparse Attention Distillation
The paper discusses a method for simplifying the replacement of self-attention mechanisms in transformer models through sparse attention distillation. It highlights the potential for reducing computational costs while maintaining performance by substituting complex attention layers with simpler sequential modules. The authors demonstrate that replacing layers with sparser attention results in smaller accuracy drops compared to denser layers.
- ▪Self-attention in transformers is computationally expensive due to quadratic token interaction costs.
- ▪The proposed method allows for efficient attention replacement, reducing parameter size and latency.
- ▪Controlled experiments show that substituting sparser attention layers incurs smaller accuracy drops than denser ones.
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Computer Science > Machine Learning arXiv:2605.18865 (cs) [Submitted on 15 May 2026] Title:From Sparsity to Simplicity: Enabling Simpler Sequential Replacements via Sparse Attention Distillation Authors:Yuxin Ren, Maxwell D Collins, Miao Hu, Huanrui Yang View a PDF of the paper titled From Sparsity to Simplicity: Enabling Simpler Sequential Replacements via Sparse Attention Distillation, by Yuxin Ren and 3 other authors View PDF HTML (experimental) Abstract:Self-attention serves as the core foundation of large-scale transformer pretraining, but its quadratic token interaction cost makes inference expensive. Replacing attention with simpler sequential modules is appealing, yet naive substitution is often lossy, especially at larger scales.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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