Title: Sparse Attention Adaptation for Long Reasoning
Primary area: foundation or frontier models, including LLMs
Keywords: LLM, Sparse Attention, Reasoning

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.