I'm out of time but "reasoning input tokens" from fortune 5000 engineers sounds like a lobotomized LSD dream, would you care on elaborating how you distinguish between reasoning and non-reasoning? vs "question on duty"?
- Take in the user query (input tokens)
- Break that into a game plan. Ex: "Based on user query: {query} generate a plan of action." (reasoning tokens)
- Answer (output tokens)
Because the reasoning step runs in a loop until it's run through it's action plan, it frequently uses way more tokens than the input/output step.
I have sooo many issues with the naming scheme of this """""AI"""" industry", it's crazy!
So the LLM gets a prompt, then creates a scheme to pull pre-weighted tokens post-user-phrasing, the constituents of which (the scheme) are called reasoning tokens, which it only explicitly distinguishes as such because there are hundreds or even thousands of output tokens to the hundreds and/or thousands of potential reasoning input tokens that were (almost) equal to the actually chosen reasoning input tokens based on the more or less adequately phrased question/prompt given ... as input ... by the user ...
It is essentially a way to expand the prompt further. You can achieve the same exact thing by turning off the “thinking” feature and just being more detailed and step by step in your prompt but this is faster.
My guess is that the next evolution of this will be models that do an edit or review step after to catch if any of the constraints were broken. But best I can tell a reasoning model can be approximated by doing two passes of a non-reasoning model: first pass you give it the user prompt with instructions that boil down to “make sense of this prompt and formulate a plan” and the second pass you give it the original prompt, the plan, and an explanation that the plan is to implement the original prompt using the plan.