I tried it and it performed poorly on my private benchmark problems. The overthinking problem is real, it takes 5-10x the reasoning tokens than comparable models. It is a sign of inadequate training of the base model and it is using more reasoning tokens to compensate for that. I also noticed that it is likely to get into somewhat repetitive reasoning and forgetting about some user requirements, suggesting that it could be the side effects of using 3:1 linear attention vs full attention.