This repository implements Efficient Neural Architecture Search (ENAS), an AutoML technique that uses a reinforcement learning controller to search for optimal neural network architectures. The controller learns to sample architectures from a shared computation graph, dramatically reducing GPU-hours compared to traditional NAS. It demonstrates state-of-the-art performance on Penn Treebank language modeling by discovering optimal recurrent cell structures.