LLMs write long-winded replies because more token output = more chances for the AI to reason its way to a satisfactory response. The model architecture for thes

LLMs write long-winded replies because more token output = more chances for the AI to reason its way to a satisfactory response. The model architecture for these systems has no recursive compute - i.e. they take in tokens, do a fixed amount of compute, then spit out more tokens; so the only way for a model to take longer and think more is to spend more output tokens on thinking. o1, DeepSeek-R1, and the like formalize this with a hidden scratchpad and additional tuning to make the model write out an entire thought process. I suppose this would also mean that the output doesn't have to be as long - i.e. maybe reasoning models could give you just the answer, and a few reasons why, and then you open up the thought process if you want the nitty gritty. But that also goes against OpenAI's whole "we can't tell you what's in the reasoning tokens because they're uncensored" shtick.