Sliding-Window Attention Beats Linear Attention in Long-Context Tasks
iScienceLuvr · x · 2026-08-31
Research shows that Sliding Window Attention (SWA) with sinks performs as well as or better than post-trained Linear Attention models across multiple LLMs and downstream tasks. In long-context reasoning tasks like Needle-in-a-Haystack and BABILong, SWA achieves 2 to 10 times higher performance. SWA requires no post-training, is extremely fast, and needs low memory, making it a cost-effective solution for reducing inference costs.
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