Sliding Window Attention Outperforms Linear Attention in LLMs
August 27, 2026
Sliding Window Attention (SWA) with sinks matches or exceeds the performance of post-trained Linear Attention models. This approach addresses the memory and energy costs of quadratic attention scaling without the complexities of linear approximations.
HOW THIS AFFECTS YOU
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builderYou can implement SWA to reduce memory overhead in long-context applications.
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researcherThis suggests SWA is a superior baseline for addressing quadratic scaling issues.