Trained on vast corpora of human language, language models demonstrate emergent human-like reasoning abilities. Yet they are still far from true intelligence, which opens up intriguing opportunities to explore the parallels of humans and model behaviors. In this work, we study the ability to skip steps in reasoning - a hallmark of human expertise developed through practice. Unlike humans, who may skip steps to enhance efficiency or to reduce cognitive load, models do not inherently possess such motivations to minimize reasoning steps. To address this, we introduce a controlled framework that stimulates step-skipping behavior by iteratively refining models to generate shorter and accurate reasoning paths. Empirical results indicate that models can develop the step skipping ability under our guidance. Moreover, after fine-tuning on expanded datasets that include both complete and skipped reasoning sequences, the models can not only resolve tasks with increased efficiency without sacrificing accuracy, but also exhibit comparable and even enhanced generalization capabilities in out-of-domain scenarios. Our work presents the first exploration into human-like step-skipping ability and provides fresh perspectives on how such cognitive abilities can benefit AI models.

翻译：在大量人类语言语料库上训练的语言模型展现出类人的推理能力。然而，它们距离真正的智能仍有差距，这为探索人类与模型行为之间的相似性提供了有趣的研究机会。本文研究了推理过程中跳过步骤的能力——这是人类通过实践发展出的专业能力的标志。与人类可能为了提高效率或减轻认知负荷而跳过步骤不同，模型本身并不具备最小化推理步骤的内在动机。为此，我们引入了一个受控框架，通过迭代优化模型生成更短且准确的推理路径，以激发其跳过步骤的行为。实验结果表明，在我们的引导下，模型能够发展出跳过步骤的能力。此外，在包含完整推理序列和跳过步骤的推理序列的扩展数据集上进行微调后，模型不仅能在不牺牲准确性的前提下以更高效率解决问题，还能在领域外场景中展现出相当甚至更强的泛化能力。本研究首次对人类式的步骤跳过能力进行了探索，并为这类认知能力如何使人工智能模型受益提供了新的视角。