# should GBDT use the same loss functions for each weak learner

should GBDT use the same loss functions for each weak learner? The answer seems yes. I understand as the principle of GBDT is gradient descent. So if we use different loss functions, the gradient descent will not make sense. Am I right? Just want a confirmation.

I assume GBDT means gradient boosting with decision trees. GBDT is not a popular acronym. If you use full words may be more people will understand.