Stop When Further Reasoning Won't Help: Attention-State Adaptive Generation in Reasoning Models

• URL: http://arxiv.org/abs/2606.15070v1
• Date: Sat, 13 Jun 2026 02:58:29 GMT
• Title: Stop When Further Reasoning Won't Help: Attention-State Adaptive Generation in Reasoning Models
• Abstract summary: Large reasoning models (LRMs) can solve complex problems through explicit chain-of-thought reasoning processes.<n>LRMs often suffer from overthinking, resulting in redundant token outputs and degraded accuracy.<n>We propose ASAG, which infers the model's reasoning state and adaptively adjusts the generation strategy.
• Score: 11.158010513386666
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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