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About this paper
Mudit Gaur, Prashant Trivedi, Shuchin Aeron, Amrit Singh Bedi, George K. Atia, Vaneet Aggarwal
The authors analyze finite-data, finite-update training for flow-matching generators. They separate dataset size N, which affects statistical error, from SGD updates T, which affect optimization error. Their theorem bounds exact learned ODE terminal Wasserstein-2 error, giving epsilon^-4 samples and epsilon^-2 updates for error O(epsilon) when approximation and tail errors are small and stated assumptions hold.
Summary by alphaXiv.
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