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.

大型语言模型可以通过为其预测生成理由来解决复杂的推理任务。通过将这些功能融入到较小、紧凑的模型中，可以促进为特定任务量身定制的专门、经济高效的模型的创建。然而，较小的模型在复杂的推理任务中常常面临挑战，并且通常会偏离正确的推理路径。我们发现，只有在恰当的时机介入，大型语言模型才能指导较小的模型并将它们带回正确的推理路径。我们发现，较小的模型在推理方面主要因为难以启动过程而失败，引导它们朝正确的方向发展可以使性能提高 100% 以上。我们探索不同的模型大小并评估提供指导以改善较小模型推理能力的益处。