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arXiv · 2506.08889

SeerAttention-R: Sparse Attention Adaptation for Long Reasoning

Yizhao Gao·Shuming Guo·Shijie Cao·Yuqing Xia·Yu Cheng·Lei Wang·Lingxiao Ma·Yutao Sun·Tianzhu Ye·Li Dong·Hayden Kwok-Hay So·Yu Hua·Ting Cao·Fan Yang·Mao Yang
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Abstract

We introduce SeerAttention-R, a sparse attention framework specifically tailored for the long decoding of reasoning models. Extended from SeerAttention, SeerAttention-R retains the design of learning attention sparsity through a self-distilled gating mechanism, while removing query pooling to accommodate auto-regressive decoding. With a lightweight plug-in gating, SeerAttention-R is flexible and can be easily integrated into existing pretrained model without modifying the original parameters. We demonstrate that SeerAttention-R, trained on just 0.4B tokens, maintains near-lossless reasoning accuracy with 4K token budget in AIME benchmark under large sparse attention block sizes (64/128). Using TileLang, we develop a highly optimized sparse decoding kernel that achieves near-theoretical speedups of up to 9x over FlashAttention-3 on H100 GPU at 90% sparsity. Code is available at: https://github.com/microsoft/SeerAttention.

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Yizhao Gao, Shuming Guo, Shijie Cao, Yuqing Xia, Yu Cheng, Lei Wang, Lingxiao Ma, Yutao Sun, Tianzhu Ye, Li Dong, Hayden Kwok-Hay So, Yu Hua, Ting Cao, Fan Yang, Mao Yang. 2025-06-10. SeerAttention-R: Sparse Attention Adaptation for Long Reasoning. https://arxiv.org/abs/2506.08889

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