Learning to solve small instances of a problem should help in solving large instances. Unfortunately, most neural network
architectures do not exhibit this form of scalability. Our Multi-Dimensional Recurrent LSTM Networks, however, show a high
degree of scalability, as we empirically show in the domain of flexible-size board games. This allows them to be trained from
scratch up to the level of human beginners, without using domain knowledge.