动态思维链让推理更智能，省时省资源。
Dynamic Chain-of-Thought: Towards Adaptive Deep Reasoning
- 根据任务难度动态调整推理步数和时间
- 推理时间、步骤和令牌消耗均显著降低
- 适合需要高效深度推理的场景
为减少长程思维链（CoT）中计算冗余和奖励延迟带来的资源消耗，本文提出动态思维链（D-CoT），实现推理时长与步骤的自适应调整。通过基于Python 3.13 IDLE与GPTs模拟器的仿真实验，结合DeepSeek R1作为对照组，在MIT OpenCourseWare线性代数考试题上的测试表明，D-CoT在推理时间、思维链长度（推理步骤）和令牌数量三项指标上均优于传统长程CoT，显著降低计算资源消耗。研究结果对深度推理优化具有参考价值，可为未来动态推理框架提供思路。
原文摘要 · Abstract (English)
To reduce the cost and consumption of computing resources caused by computational redundancy and delayed reward assignment in long CoT, this research proposes the dynamic chain-of-thought (D-CoT) with adaptive reasoning time and steps. The researcher used simulation experiment to simulate the integration of D-CoT through Python 3.13 IDLE combined with a Python simulator based on GPTs. At the same time, the researcher used DeepSeek R1 as a control group to test and compare the performance of the D-CoT simulator in processing MIT OpenCourseWare's linear algebra exam questions. Experimental results show that D-CoT is better than DeepSeek R1 based on long CoT in three indicators: reasoning time, CoT length (reasoning steps) and token count, which achieves a significant reduction in computing resource consumption. In addition, this research has potential value in deep reasoning optimization that is used as a reference for future dynamic deep reasoning frameworks.
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