Title: Test-Time Training on Video Streams. Abstract: We investigate visual generalization video streams instead of independent images, since the former is closer to the smoothly changing environments where natural agents operate. Traditionally, single-image models are tested on videos as collections of unordered frames. We instead test on each video in temporal order, making a prediction on the current frame before the next arrives, after training at test time on frames from the recent past. To perform test-time training without ground truth labels, we leverage recent advances in masked autoencoders for self-supervision. We improve performance on various real-world applications. We also discover that forgetting can be beneficial for test-time training, in contrast to the common belief in the continual learning community that it is harmful.