The paper proposes an interesting semisupervised approach to neural architecture search: Using architecture accuracy prediction function to to train the controller (architecture generator), and shows that such approach yields efficiency improvements. Reviewers generally agree on simplicity of this method and good experimental evaluation. Reviewers 3, 4 point out a number of missing comparisons however many of these are addressed in the rebuttal. It would also be good to understand why this method work, since as reviewer points out, no new information is added by the evaluation network - which on the other hand makes the experimental confirmation interesting. Overall this is an interesting and simple method with good evaluation and results.