Because they are an integral part of the solution generation.

By distilling, you are training a model via Reinforcement Learning (RL) to mimic the answer of a bigger model. To do that, you need all the steps that contributed to generating an answer.

To give you a better idea, imagine teaching a student how to solve math problem:

1. You give it the problem and the answer only (no thinking trace) 2. You give it the problem, the intermediary steps and the answer (full transcript)

I think you can agree that the second method is more likely to give a well-informed student.