as a powerful generative modeling
paradigm based on a simulation-free training objective instead of a score-based
one used in DMs. Typical FM approaches rely on a Gaussian distribution prior,
which induces curved, conditional probability paths between the prior and
target data distribution. These curved paths pose a challenge for the Ordinary
Differential Equation (ODE) solver, requiring a large number of inference calls
to the flow prediction network. To address this issue, we present Learned
Distribution-