and then the gradient descent is actually driving you away from it and training accuracy is growing you know this is a regime where you're interpolating the training accuracy goes to one but the test error is getting worse so actually after one iteration you were perfectly optimal but then the gradient is driving you away from the perfect generalization not perfectionist the optimal generalization point so that's a kind of specular property of this particular two cluster model that we that we discovered in this paper and