From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step
Keywords: implicit reasoning, chain of thought
TL;DR: teach language models to internalize chain-of-thought reasoning by gradually removing intermediate steps and finetuning.
Abstract: When leveraging language models for reasoning tasks, generating explicit chain-of-thought (CoT) steps often proves essential for achieving high accuracy in final outputs. In this paper, we investigate if models can be taught to internalize these CoT steps. To this end, we propose a simple yet effective method for internalizing CoT steps: starting with a model trained for explicit CoT reasoning, we gradually remove the intermediate steps and finetune the model. This process allows the model to internalize the intermediate reasoning steps, thus simplifying the reasoning process while maintaining high performance. Our approach enables training a GPT-2 Small model to solve 20-by-20 multiplication with 99.5% accuracy while being 26 times faster than explicit CoT, whereas standard training cannot solve beyond 4-by-4 multiplication. Furthermore, our method proves effective on larger language models, such as Mistral 7B, achieving over 50% accuracy on GSM8K without producing any intermediate steps.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 7676
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