Understanding Chain-of-Thought (CoT) Reasoning: The Core Behind OpenAI’s o1 Model
Author(s): Shivam Mohan
Originally published on Towards AI.
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Chain-of-Thought (CoT) reasoning is an approach that significantly enhances the reasoning abilities of large language models (LLMs) by breaking down complex problems into smaller, manageable steps. By encouraging a model to explain its intermediate thought process, CoT helps it arrive at more accurate solutions, particularly in tasks requiring arithmetic, commonsense, or symbolic reasoning.
At its core, chain-of-thought reasoning is about making a model not just answer questions but also explain how it reached its answer. This method mimics how humans solve complex problems — by breaking them down into smaller steps and reasoning through each step.
For example, when asked how many tennis balls Roger has after buying 2 more cans (each containing 3 balls) in addition to his 5 tennis balls, a model using CoT reasoning would explain its reasoning like this:
Roger started with 5 balls. 2 cans of 3 tennis balls each give 6 more balls. 5 + 6 = 11. The answer is 11.
Contrast this with standard reasoning, where the model would directly answer 11 without explaining the steps involved.
1. Better Problem Decomposition: CoT allows models to break down multi-step problems into simpler intermediate steps. This… Read the full blog for free on Medium.
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