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SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking

50 upvotes · 11 SEP 2026 · Zhiwei Li, Lei Zhu, Hao Gu et al.

This paper proposes a new method to sparsify attention in Transformers, called Simple Attention Sparsification (SAS), which optimizes context ranking end-to-end with the language modeling loss. Practitioners might care because SAS can improve performance on downstream tasks by using attention budgets more effectively.

attention sparsificationTransformersend-to-end optimizationcontext rankingcontinual learning