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Sliding Window Attention Outperforms Linear Attention in LLMs

By PulseAugur Editorial · [5 sources] ·

A new research paper indicates that sliding window attention (SWA) with attention sinks performs as well as or better than linear attention models for large language models (LLMs). The study, published on arXiv and highlighted by Hugging Face, found that SWA significantly outperforms linear attention on long-context reasoning tasks like Needle-in-a-Haystack and BABILong, achieving 2 to 10 times higher performance. SWA offers a more efficient and cost-effective solution for LLM inference, requiring no post-training, low memory, and high speed, making it a recommended alternative to linear attention models. AI

IMPACT Sliding window attention offers a more efficient and cost-effective solution for LLM inference, potentially reducing memory and energy consumption.

RANK_REASON Research paper comparing two attention mechanisms for LLMs.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

Sliding Window Attention Outperforms Linear Attention in LLMs

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Research paper comparing two attention mechanisms for LLMs.
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COVERAGE [5]

  1. arXiv cs.CL TIER_1 English(EN) · Alexia Jolicoeur-Martineau, Rhea Sanjay Sukthanker, Pashmina Cameron, Emy Gervais ·

    Sliding-window beats linear attention

    arXiv:2608.28444v1 Announce Type: new Abstract: Due to the nature of quadratic attention, Large Language Models (LLMs) consume a lot of memory and energy. Every new token costs more than the previous one. For each additional token, the keys and values must be stored in memory ind…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Sliding-window beats linear attention

    Sliding window attention with sinks outperforms post-trained linear attention on long-context tasks without requiring retraining, offering a cheaper and more reliable inference solution.

  3. dev.to — LLM tag TIER_1 English(EN) · Prabhakar Chaudhary ·

    Technical Analysis: Sliding-Window Beats Linear Attention in Efficiency Benchmarks

    <h1> Why Sliding-Window Attention Outperforms Linear Attention for Efficient Inference </h1> <p>The pursuit of infinite context windows in Large Language Models (LLMs) has led to a proliferation of complex architectural modifications. As models scale, the standard quadratic atten…

  4. r/MachineLearning TIER_1 English(EN) · /u/Justgototheeffinmoon ·

    Sliding-window attention beats linear on long-context reasoning [R]

    <!-- SC_OFF --><div class="md"><p>Sliding Window Attention with sinks, one of the simplest existing fixes for the quadratic-cost problem in LLMs, holds up as well or better than the linear-attention variants labs have been spending post-training compute to produce. That is the cl…

  5. r/LocalLLaMA TIER_1 English(EN) · /u/woadwarrior ·

    Sliding-window beats linear attention

    <!-- SC_OFF --><div class="md"><p>Interesting new <a href="https://arxiv.org/abs/2608.28444">paper</a> from Alexia Jolicoeur-Martineau (of Tiny Recursive Model fame) and collaborators. They seem to be able to replace quadratic attention with sliding window attention + attention s…

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