Less Is More: Training-Free Sparse Attention with Global Locality for Efficient Reasoning

• URL: http://arxiv.org/abs/2508.07101v1
• Date: Sat, 09 Aug 2025 21:10:33 GMT
• Title: Less Is More: Training-Free Sparse Attention with Global Locality for Efficient Reasoning
• Authors: Lijie Yang, Zhihao Zhang, Arti Jain, Shijie Cao, Baihong Yuan, Yiwei Chen, Zhihao Jia, Ravi Netravali,
• Abstract summary: We introduce LessIsMore, a training-free sparse attention mechanism for reasoning tasks.<n>LessIsMore aggregates token selections from local attention heads with recent contextual information.<n>It achieves a $1.13times$ end-to-end speed-up compared to existing sparse attention methods.
• Score: 12.808478519221577
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large reasoning models achieve strong performance through test-time scaling but incur substantial computational overhead, particularly from excessive token generation when processing short input prompts. While sparse attention mechanisms can reduce latency and memory usage, existing approaches suffer from significant accuracy degradation due to accumulated errors during long-generation reasoning. These methods generally require either high token retention rates or expensive retraining. We introduce LessIsMore, a training-free sparse attention mechanism for reasoning tasks, which leverages global attention patterns rather than relying on traditional head-specific local optimizations. LessIsMore aggregates token selections from local attention heads with recent contextual information, enabling unified cross-head token ranking for future decoding layers. This unified selection improves generalization and efficiency by avoiding the need to maintain separate token subsets per head. Evaluation across diverse reasoning tasks and benchmarks shows that LessIsMore preserves -- and in some cases improves -- accuracy while achieving a $1.1\times$ average decoding speed-up compared to full attention. Moreover, LessIsMore attends to $2\times$ fewer tokens without accuracy loss, achieving a $1.13\times$ end-to-end speed-up compared to existing sparse attention methods.

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