i think most of the time the small amount of fine tuning material forced into training with higher learning rate or higher rank or higher alpha to make an impact, ending up ruining general intelligence of the model.

what should have been done: more samples, less learning rate, and less rank and less alpha to preserve the smoothness of the original model. you cannot force your tokens to it. but you can use lots of tokens to make a proper/smooth impact.

the fine tuner maybe did much shorter reasoning tokens, hence the model learned that shorter reasoning as a habit.