Deep learning has advanced by scaling datasets, models, and training computation. At the same time applications have broadened to many kinds of data (personal, scientific, …) and deployments (in clouds, on cars, …). Will these all be solved by more data, parameters, and training? Test-time updates are complementary, and can help on both foundation model servers and edge devices. This workshop examines train-time vs. test-time updates across scales by test-time adaptation, continual learning, in-context learning, and post-training editing.