Implicit Chain of Thought Reasoning via Knowledge Distillation

TL;DR

This paper introduces an implicit reasoning method for language models that leverages hidden states distilled from explicit chain-of-thought models, enabling efficient multi-step reasoning without generating intermediate natural language steps.

Contribution

It proposes a novel implicit reasoning approach using knowledge distillation from explicit chain-of-thought models, improving reasoning efficiency and capability.

Findings

Enables solving complex math problems previously unsolvable without explicit reasoning.

Achieves reasoning speed comparable to models without chain-of-thought.

Demonstrates effectiveness on multi-digit multiplication and grade school math datasets.

Abstract

To augment language models with the ability to reason, researchers usually prompt or finetune them to produce chain of thought reasoning steps before producing the final answer. However, although people use natural language to reason effectively, it may be that LMs could reason more effectively with some intermediate computation that is not in natural language. In this work, we explore an alternative reasoning approach: instead of explicitly producing the chain of thought reasoning steps, we use the language model's internal hidden states to perform implicit reasoning. The implicit reasoning steps are distilled from a teacher model trained on explicit chain-of-thought reasoning, and instead of doing reasoning "horizontally" by producing intermediate words one-by-one, we distill it such that the reasoning happens "vertically" among the hidden states in different layers. We conduct experiments on a multi-digit multiplication task and a grade school math problem dataset and find that this approach enables solving tasks previously not solvable without explicit chain-of-thought, at a speed comparable to no chain-of-thought.