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Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

Yuan et al., 2025, Native Sparse Attention (Proceedings of the 63rd Annual Meeting of ACL (Volume 1: Long Papers))

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2.3.3 Sliding Window

  1. Why does NSA have a separate sliding-window branch?
    Local patterns are learned fast and can dominate, keeping the model from learning to use compressed and selected tokens. A dedicated window of recent tokens (512) handles local context, with separate keys and values per branch to prevent shortcut learning.
    • Yuan et al., 2025, Native Sparse Attention (Proceedings of the 63rd Annual Meeting of ACL (Volume 1: Long Papers)), §2.3.3, p. 23082

Source

  • Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Yuxing Wei, Lean Wang, Zhiping Xiao, Yuqing Wang, Chong Ruan, Ming Zhang, Wenfeng Liang and Wangding Zeng. 2025. Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 23078–23097. License: CC BY 4.0. Flashcards adapted from this work. doi:10.18653/v1/2025.acl-long.1126 https://aclanthology.org/2025.acl-long.1126/