New theory explains Chain-of-Thought generalization in LLMs

A new paper explores the theoretical underpinnings of Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs), focusing on its out-of-distribution (OOD) generalization capabilities. The research posits that CoT achieves OOD generalization by composing reusable transitions through repeated application of a shared single-step predictor. Theoretical analysis suggests that the OOD task risk is influenced by the sum of in-distribution sub-task risks, scaled by a factor related to the number of output tokens per step, and an estimation term dependent on the number of reasoning steps and training samples. Experiments conducted on Atom-Task and GSM8K datasets appear to support these theoretical findings, highlighting the importance of explicit state tracking for effective CoT generalization. AI

IMPACT Provides a theoretical framework for understanding and potentially improving LLM reasoning and generalization capabilities.

RANK_REASON Academic paper detailing theoretical analysis and experimental validation of LLM reasoning mechanisms. [lever_c_demoted from research: ic=1 ai=1.0]

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• Atom-Task
• GSM8K
• Large Language Models
• next-token prediction

• paper
• model release

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