CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective

Chain-of-Thought (CoT) does not elicit genuine, abstract reasoning. Instead, we argue that Chain-of- Thought (CoT) functions as a powerful structural constraint that guides Large Language Models (LLMs) to imitate the form of reasoning. By forcing the generation of intermediate steps, Chain-of-Thought (CoT) leverages the model’s immense capacity for sequence prediction and pattern matching, effectively constraining its output to sequences that resemble coherent thought processes. We dissect the mechanisms underlying Chain-of-Thought (CoT)’s efficacy through the lens of constrained imitation learning, highlighting its reliance on recognizing and reproducing familiar reasoning schemata rather than constructing novel inferential pathways or manipulating abstract symbolic representations. Our theoretical analysis explores the implications of this view, including inherent limitations in generalization to truly novel problems, brittleness to subtle prompt variations, and the potential for ”reasoning fallacies” that mimic correct form but lack semantic grounding.

Does Chain-of-Thought (CoT) truly enable models to reason abstractly, or does it merely guide them to produce outputs that look like reasoning

we advance the latter hypothesis from a theoretical standpoint. We posit that Chain-of-Thought (CoT) is not a catalyst for emergent, abstract reasoning. Instead, we conceptualize Chain-of-Thought (CoT) as a highly effective structural constraint that leverages the Large Language Model (LLM)’s core strength: imitating complex sequential patterns observed during pre-training. The "step-by-step" instruction acts as a tight constraint, forcing themodel to generate intermediate textual tokens that mimic the form and flow of reasoning processes it has encountered in its vast training corpus. The resulting output, while often correct and appearing well-reasoned, may stem from sophisticated pattern matching and interpolation rather than a deeper, symbolic, or causal understanding of the problem.