标题： 逻辑熵自适应停止启发式方法用于高效的思维链推理 显示英文标题

标题： Logit-Entropy Adaptive Stopping Heuristic for Efficient Chain-of-Thought Reasoning

摘要： 链式思维（CoT）提示是使大型语言模型具备复杂推理能力的关键技术。 然而，生成完整且固定长度的推理过程在计算上是浪费的，增加了令牌使用量和延迟。 我们引入LEASH：Logit-Entropy Adaptive Stopping Heuristic，这是一种无需训练的解码算法，能够自适应地停止推理生成。 LEASH监控两个内在信号：令牌级熵的斜率和top-logit边际的改进。 一旦这两个信号达到平稳状态，表示模型已经达到了稳定的推理状态，就会终止生成。 在GSM8K和AQuA-RAT基准上的四个指令调优模型中，LEASH将平均令牌生成量减少了30--35%，延迟减少了27%，而相对于CoT的准确性下降了10个百分点。 LEASH与模型无关，不需要额外的训练或监督，为CoT解码提供了一种简单高效的替代方案。 显示英文摘要

摘要： Chain-of-Thought (CoT) prompting is a key technique for enabling complex reasoning in large language models. However, generating full, fixed-length rationales is computationally wasteful, inflating both token usage and latency. We introduce LEASH: Logit-Entropy Adaptive Stopping Heuristic, a training-free decoding algorithm that adaptively halts rationale generation. LEASH monitors two intrinsic signals: the slope of token-level entropy and the improvement in the top-logit margin. It terminates the generation once both signals plateau, indicating the model has reached a stable reasoning state. Across four instruction-tuned models on the GSM8K and AQuA-RAT benchmarks, LEASH reduces average token generation by 30--35% and latency by 27%, while incurring a 10 p.p. accuracy drop relative to CoT. LEASH is model-agnostic and requires no additional training or supervision, offering a simple and efficient alternative to CoT decoding.