Stop When Further Reasoning Won't Help: Attention-State Adaptive Generation in Reasoning Models
Summary
This paper proposes ASAG, a training-free method that adaptively stops reasoning in large reasoning models based on attention distributions, reducing token usage by ~40% while improving accuracy by 3.2% on benchmarks using DeepSeek-R1-Distill and Qwen3 models.
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# Stop When Further Reasoning Won't Help: Attention-State Adaptive Generation in Reasoning Models Source: [https://arxiv.org/abs/2606.15070](https://arxiv.org/abs/2606.15070) [View PDF](https://arxiv.org/pdf/2606.15070) > Abstract:By incorporating test\-time compute scaling, large reasoning models \(LRMs\) can solve complex problems through explicit chain\-of\-thought \(CoT\) reasoning processes\. However, they often suffer from overthinking, resulting in redundant token outputs and degraded accuracy\. Current methods to mitigate this issue remain limited: training\-based approaches require substantial computational resources, while training\-free methods rely on well\-crafted prompts or unreliable confidence signals\. In this work, we investigate early stopping from the perspective of attention distributions and propose a simple method, ASAG, which infers the model's reasoning state and adaptively adjusts the generation strategy\. The proposed framework is training\-free and plug\-and\-play, enabling seamless integration into existing LRMs\. Extensive experiments on nine benchmarks demonstrate consistent improvements across mainstream LRMs with varying parameter scales, including the DeepSeek\-R1\-Distill and Qwen3 series\. Specifically, ASAG improves average accuracy by 3\.2% while reducing the number of generated tokens by nearly 40% across all reasoning tasks on Qwen3\-8B\. ## Submission history From: Jiakai Li \[[view email](https://arxiv.org/show-email/65b659c6/2606.15070)\] **\[v1\]**Sat, 13 Jun 2026 02:58:29 UTC \(1,220 KB\)
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