Akarsh Kumar and Phillip Isola propose training RNNs without standard backpropagation through time, sidestepping the long-sequence credit-assignment cost that has made recurrent models hard to scale. If it holds up, it reopens recurrent architectures as an efficiency play against attention. Worth watching for efficient long-context modeling.