Title: Learn Together, Stop Apart: An Inclusive Approach To Early Stopping. Abstract: Gradient Boosting is the most popular method of constructing ensembles that allows to get state-of-the-art results on many tasks. One of the critical parameters affecting the quality of the learned model is the number of members in the ensemble or the number of boosting iterations. Unfortunately, the problem of selecting the optimal number of models still remains open and understudied. This paper proposes a new look at the optimal stop selection problem in Gradient Boosting. In contrast to the classical approaches that select a universal ensemble size using a hold--out validation set, our algorithm takes into account the heterogeneity of data in the feature space and adaptively sets different number of models for different regions of data, but it still uses the same common ensemble trained for the whole task. Experiments on SOTA implementations of Gradient Boosting show that the proposed method does not affect the complexity of learning algorithms and significantly increases quality on most standard benchmarks up to 2%.