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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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3.1 Pretraining Setup

  1. How was NSA evaluated against full attention?
    A 27B-parameter MoE model (3B active, with GQA) was pretrained on 270B tokens of 8k-length text with either full attention or NSA, then extended to 32k with YaRN, both trained to convergence, and compared on general, long-context and reasoning benchmarks.
    • Yuan et al., 2025, Native Sparse Attention (Proceedings of the 63rd Annual Meeting of ACL (Volume 1: Long Papers)), §3.1, p. 23083

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/