We don't know the specifics of GPT-o1 to judge, but we can look at open weights model for an example. Qwen-32B is a base model, QwQ-32B is a "reasoning" variant. You're broadly correct that the magic, such as it is, is in training the model into a long-winded CoT, but the improvements from it are massive. QwQ-32B beats larger 70B models in most tasks, and in some cases it beats Claude.
I just tried QwQ 32B, i didn't know about it. I used it to generate, some code GPT generated 2 days ago perfect code without even sweating.
QwQ generated 10 pages of it's reasoning steps, and the code is probably not correct. [1] includes both answers from QwQ and GPT.
Breaking down it's reasoning steps to such an excruciating detailed prose is certainly not user friendly, but it is intriguing. I wonder what an ideal use case for it would be.
