In the training phase, penalizing models not just for wrong answers, but

also for excessive length in the (CoT) thinking trace

seems to be the recipe, or at least part of the training recipe, for training a model to "think" less (and thus output less CoT tokens and thus operate faster) at approximately the same accuracy as the same model, previously trained without penalizing excessive length wrong answers in the thinking trace...

Anyway, well done BottleCap AI!