Large Language Models (LLMs) can solve complex reasoning tasks by generating rationales for their predictions. Distilling these capabilities into a smaller, compact model can facilitate the creation of specialized, cost-effective models tailored for specific tasks. However, smaller models often face challenges in complex reasoning tasks and often deviate from the correct reasoning path. We show that LLMs can guide smaller models and bring them back to the correct reasoning path only if they intervene at the right time. We show that smaller models fail to reason primarily due to their difficulty in initiating the process, and that guiding them in the right direction can lead to a performance gain of over 100%. We explore different model sizes and evaluate the benefits of providing guidance to improve reasoning in smaller models.

翻译：大型语言模型能够通过生成推理过程来解决复杂的推理任务。将这些能力提炼到更小、更紧凑的模型中，有助于创建针对特定任务的专用且成本效益高的模型。然而，较小的模型在复杂推理任务中常面临挑战，并且往往偏离正确的推理路径。我们证明，大型语言模型只有在正确时机进行干预时，才能引导较小模型并使其回归正确的推理路径。我们发现，较小模型推理失败的主要原因是难以启动推理过程，而引导其向正确方向前进可使性能提升超过100%。我们探究了不同模型规模，并评估了提供引导以改善较小模型推理能力的益处。