when you distill a thinking LLM past its capacity it will default to overthinking because during training that was the only way for a chance at a reward on many tasks.
you can generally avoid this if you specialize it on a domain that is within its capacity.
I think that in this case there is also the problem of trying to transfer MoE-style reasoning into a dense model. I mean, MoE needs reasoning to walk multiple experts, but dense model already has all the weights. So when you push it hard to reproduce the MoE traces, you are effectively asking a small mouse to role-play as an anthill. Not great.