I don’t think there’s consensus. Some papers have shown that just giving the model more tokens improves the results, ie chain of thought allows more computation

I don’t think there’s consensus. Some papers have shown that just giving the model more tokens improves the results, ie chain of thought allows more computation to happen and that is enough to improve results. Others have argued that the smaller steps themselves are easier to solve and thus it’s easier to reach the right answer. I think CoT is important because it’s _free_. You adjust the prompt (model input) at the end of training. Magically, this seems to work. That makes it hard to beat even if you did have a clear understanding of the mechanism at a more fundamental level.