Tutorial, Lecture
How do OpenAI’s o1 and o3 models perform complex reasoning?
- OpenAI's O1 model employs a "chain of thought" reasoning mechanism to solve complex problems by decomposing them into sequential steps, mirroring human problem-solving strategies.
- This approach allows the model to recognize errors, attempt corrections, and adjust its strategy dynamically rather than generating a single direct answer.
- The "chain of thought" concept was formally introduced by Google Brain researchers in 2022.
- Without this step-by-step decomposition, Large Language Models (LLMs) typically rely on predicting the most probable next token, which often results in insufficient context for accurate calculation.
- In a specific 2022 paper example involving a math problem (a pizza cut into 8 slices where 3 are eaten by John and 2 by a friend), the model was instructed to:
- Identify the total number of slices (8).
- Calculate the total slices consumed by John and his friend (3 + 2 = 5).
- Subtract the consumed slices from the original total to determine the remainder (8 - 5 = 3).