想减少推理模型输出废话？ASAG免费即插即用，在Qwen3-8B上准确率升3.2%还省近40%token，实打实的效果。
大推理模型（LRM）常因过度思考生成冗余token，降低准确率。ASAG方法通过分析注意力分布推断推理状态，自适应调整生成策略。该方法无需训练，可即插即用，在DeepSeek-R1-Distill和Qwen3系列等主流模型上测试。在Qwen3-8B上，ASAG平均准确率提升3.2%，生成token减少约40%。
Stop When Further Reasoning Won't Help: Attention-State Adaptive Generation in Reasoning Models
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.