通过自优化思维向量实现可控数学推理
Controllable Mathematical Reasoning via Self-Optimizing Thought Vectors
AI总结：
本文提出利用自优化思维向量与熵最小化实现可控数学推理，在GSM8K上以Gemma-2-9B达到90.1%准确率，无需外部奖励标注，验证了框架的有效性。
摘要：
我们提出了一种新颖的可控数学推理方法，该方法利用熵最小化的自优化思维向量。我们的方法引入了可学习的思维向量，能够动态调节大型语言模型的内部推理过程。在GSM8K上使用Gemma-2-9B，我们达到了90.1%的准确率和0.42的可控性得分，证明了基于熵的奖励能有效引导聚焦的推理模式，而无需外部奖励标注。我们的分析揭示了不同控制条件下明显的思维向量聚类和一致的低熵分布，验证了我们用于可控AI推理的框架。
英文摘要：
We present a novel approach for controllable mathematical reasoning that leverages self-optimizing thought vectors with entropy minimization. Our method introduces learnable thought vectors that dynamically modulate the internal reasoning process of large language models. Using Gemma-2-9B on GSM8K, we achieve 90.1% accuracy with a controllability score of 0.42, demonstrating that entropy-based rewards effectively guide focused reasoning patterns without requiring external reward annotations. Our analysis reveals distinct thought vector clusters and consistent low-entropy distributions across control conditions, validating our framework for controllable AI reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
