Since reasoning tokens are just text, I think the models have learned to squeeze in some computation in their output writing as well. So they're incentivized to be correct but long-winded, as it gives them more time to think. It's kind of the equivalent of filler words for humans, except LLMs can actually word-vomit something intelligible.