Language models (LMs) have demonstrated remarkable performance on downstream
tasks, using in-context exemplars or human instructions. Recent works have
shown that chain-of-thought (CoT) prompting can elicit models to solve complex
reasoning tasks, step-by-step. However, the efficacy of prompt-based CoT
methods is restricted to very large LMs such as GPT-3 (175B), thus limiting
deployability. In this paper, we revisit the fine-tuning approach to enable
complex reasoning in smaller LMs, optimized to efficiently perform a specific
task. We propose Fine-tune-CoT, a method that leverages the capabilities of
very large LMs to generate reasoning samples and teach smaller models via
fine-tuning. We evaluate our method on publicly available LMs across a wide
range of complex tasks and model sizes. We find that Fine-tune-CoT enables
substantial reasoning capability in small models, whereas previous prompt-based
baselines exhibit near-random performance. Student models can even outperform
the teacher in some tasks while reducing model size requirements by several
orders of magnitude. We conduct extensive ablations and sample studies to
understand the reasoning capabilities of student models. We also identify
several important nuances that have been overlooked in concurrent fine-tuning
works on CoT and address them in our analysis.

本研究提出了 Fine-tune-CoT 方法，该方法利用非常大的语言模型以生成推理样本并通过微调来教授较小的模型，从而在较小的模型中实现了大量的推理能力优化。