it's learning throughout training to maximize the accuracy on a held out validation set this is a really interesting use of meta learning and this gradient through gradient training to have the teacher-student paradigm where the teacher is taking apart this different component of the supervised learning framework particularly in this case the target distribution and then the student is learning with the teacher network in this simultaneous I like dual optimization or coevolution framework of training these two models in the meta