DARTS implements continuous relaxation to make neural architecture search differentiable, enabling efficient gradient-based optimization over the architecture space. The algorithm searches for optimal operations and connections in CNNs for image classification and RNNs for language modeling, requiring only a single GPU. It produces architectures competitive with manually designed networks on CIFAR-10, ImageNet, Penn Treebank, and WikiText-2 benchmarks.