From my understanding of https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html and gradient boosting decision trees in general, I assume that for N parameters, the regressor picks a bunch of splits along each parameter, works out how applying this split partitions the data, then decides which split to pick which minimises the loss the most (for a particular quantile).