Decoding OpenAI’s Advanced Reasoning Models: A Gentle Introduction to How They Work

Author(s): Saleh Alkhalifa

Originally published on Towards AI.

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In todays fast-paced digital landscape, businesses and developers alike are constantly trying to stay ahead of the curve by seeking smarter solutions for complex challenges. This last month, the most popular topics of conversation at work and in online forums have revolved around the latest reasoning models. In particular, OpenAI’s reasoning models, o1 and o3, gained considerable traction as they are designed to “think before they answer”, and have changed the way we think and approach problem solving, coding, and even scientific research within the Language Model domain.

Reasoning models are the latest generation of Large Language Models, which incorporate unique internal processes through “Chain of Thought” reasoning before delivering a final response to a given query. Unlike traditional models that generate a response from a given input, these models break down a prompt into multiple steps and internally generate a detailed sequence of reasoning tokens. This approach allows them to tackle multi-step planning and complex logic more effectively, making them ideal for tasks such as complex problem solving, coding and debugging, and scientific research which often involves multiple steps and trains of thought.

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Published via Towards AI