Order-Optimal Sample Complexity of Rectified Flows
Rectified flow models aim to generate data by constraining transport paths to be linear between source and target distributions. The research proves that this structural constraint allows for an order-optimal sample complexity of Oe(ε-2) in Wasserstein distance approximation, matching the theoretical lower bound for generative modeling.