We improve the recently-proposed "MixMatch" semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring. Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth labels. Augmentation anchoring feeds multiple strongly augmented versions of an input into the model and encourages each output to be close to the prediction for a weakly-augmented version of the same input. To produce strong augmentations, we propose a variant of AutoAugment which learns the augmentation policy while the model is being trained. Our new algorithm, dubbed ReMixMatch, is significantly more data-efficient than prior work, requiring between $5\times$5× and $16\times$16× less data to reach the same accuracy. For example, on CIFAR-10 with 250 labeled examples we reach $93.73\%$93.73% accuracy (compared to MixMatch's accuracy of $93.58\%$93.58% with $4{,}000$4,000 examples) and a median accuracy of $84.92\%$84.92% with just four labels per class. We make our code and data open-source at https://github.com/google-research/remixmatch.