[nearbuy]

> Answer is always line 1. Reasoning comes after, never before.

This doesn't stop it from reasoning before answering. This only affects the user-facing output, not the reasoning tokens. It has already reasoned by the time it shows the answer, and it just shows the answer above any explanation.

[motoboi]

The output is part of context. The model reason but also output tokens. Force it to respond in an unfamiliar format and the next token will veer more and more from the training distribution, rendering the model less smart/useful.

[nearbuy]

It won't matter. By the time it's done reasoning, it has already decided what it wants to say.

Reasoning tokens are just regular output tokens the model generates before answering. The UI just doesn't show the reasoning. Conceptually, the output is something like:

  <reasoning>
    Lots of text here
  </reasoning>
  <answer>
    Part you see here. Usually much shorter.
  </answer>

[motoboi]

The reasoning part is not diferente from the part that goes in answer. It’s just that the model is trained to do some magical text generation with back and forth. But when it’s writing the answer part of it, each word is part of its context when generating the next. What that means is that the model does not compute then write, it generates text that guide the next generation in the general direction of the answer.

If you steer it in strange (for it, as in not seen before in training) text, you are now in out-of-distribution, very weak generalization capabilities territory.

[nearbuy]

> The reasoning part is not diferente from the part that goes in answer.

Exactly. And this instruction isn't telling it to skip the reasoning. That part is unaffected. The instruction is only for the user-visible output.

By the time the reasoning models get to writing the output you see, they've already decided what they are going to say. The answer is based on whatever it decided while reasoning. It doesn't matter whether you tell it to put the answer first or the explanation first. It already knows both by the time it starts outputting either.

You're basically hoping that adding more CoT in the output after reasoning will improve the answer quality. It won't. It's already done way more CoT while reasoning, and its answer is already decided by then.

[motoboi]

Im sorry. You are thinking in terms of one time interactions. I’m thinking about the next step in the interaction.

To understand my point, think about a prompt to tell the model “here is a very difficult code problem, answer in a single word.”

It thinks a lot and answer. You send the next prompt. At this moment, you are completely in out of distribution territory.