Is Chain-of-Thought Reasoning a Mirage?

This paper from Arizona State University's Data Mining and Machine Learning Lab investigates whether **Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) represents genuine inference or merely superficial pattern matching.** The authors hypothesize that CoT effectiveness is **bounded by the training data's distribution**, proposing that LLMs generate reasoning paths by approximating patterns seen during training. To test this, they developed **DataAlchemy**, a controlled environment for training LLMs from scratch, allowing for systematic probing across **task, length, and format generalization.** Their findings suggest that CoT reasoning is **"a brittle mirage"**, performing well only within or near training data distributions and failing significantly when pushed beyond them. This implies CoT is a sophisticated form of **structured pattern matching** rather than a true understanding of logical inference.