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A flow matching based algorithm is formulated which learns a transport map from the source to target trajectory distribution, while preserving the Markov structure, which is consistent in the population limit and derive finite-sample error bounds under mixing time assumptions.
Modern generative models are increasingly trained using model-generated signals, creating both opportunities for self-improvement and risks of collapse. We study optimal self-distillation (SD) for rectified flow (RF): given a suboptimal teacher velocity field, can a student trained on a mixture of true RF velocities an...
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