Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens

2025-08-02

AI Summary

This work challenges the authenticity of chain-of-thought (CoT) reasoning in large language models (LLMs), questioning whether it reflects genuine generalization or merely conditional imitation of training data distributions. To investigate, the authors design DataAlchemy—a controlled experimental environment featuring LLMs trained from scratch—and systematically manipulate three dimensions of distributional shift: task semantics, reasoning step length, and output format. They further employ attribution probing to analyze reasoning pathways. Empirical results demonstrate severe performance degradation under out-of-distribution conditions, revealing CoT as a “fragile illusion” critically dependent on train-test distribution alignment. The core contribution is the first data-distribution-centric deconstruction of CoT generalization, formalizing and empirically validating the “conditional imitation hypothesis.” This yields a novel conceptual framework and a reproducible evaluation methodology for characterizing fundamental limitations of LLM reasoning.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Chain-of-Thought (CoT) prompting has been shown to improve Large Language Model (LLM) performance on various tasks. With this approach, LLMs appear to produce human-like reasoning steps before providing answers (a.k.a., CoT reasoning), which often leads to the perception that they engage in deliberate inferential processes. However, some initial findings suggest that CoT reasoning may be more superficial than it appears, motivating us to explore further. In this paper, we study CoT reasoning via a data distribution lens and investigate if CoT reasoning reflects a structured inductive bias learned from in-distribution data, allowing the model to conditionally generate reasoning paths that approximate those seen during training. Thus, its effectiveness is fundamentally bounded by the degree of distribution discrepancy between the training data and the test queries. With this lens, we dissect CoT reasoning via three dimensions: task, length, and format. To investigate each dimension, we design DataAlchemy, an isolated and controlled environment to train LLMs from scratch and systematically probe them under various distribution conditions. Our results reveal that CoT reasoning is a brittle mirage that vanishes when it is pushed beyond training distributions. This work offers a deeper understanding of why and when CoT reasoning fails, emphasizing the ongoing challenge of achieving genuine and generalizable reasoning.

Problem

• Investigates if CoT reasoning is superficial or learned from data
• Examines CoT effectiveness under distribution discrepancies
• Assesses CoT failure conditions for genuine reasoning

Innovation

• Studying CoT reasoning via data distribution lens
• Designing DataAlchemy for controlled LLM training
• Revealing CoT reasoning as a brittle mirage