My impression of LLM “reasoning” is that works more like guardrails. Perhaps the space of possible responses to the initial prompt is huge and doesn’t exactly match any learned information. All the text generated during reasoning is high strength. So placing it in the context should hopefully guide answer generation towards something reasonable.

It’s the same idea as manually listing a bunch of possibly-useful facts in the prompt, but the LLM is able to generate plausible sounding text itself.

I feel like this relates to why LLM answers tend to be verbose too, it needs to put the words out there in order to stay coherent.