We present HollowFlow, a flow-based generative model based on a GNN that makes sample likelihood computations drastically more efficient.

We investigate the benefits of using path gradients to fine-tune CNFs initially trained by Flow Matching, in the setting where a target energy is known.

Learning likelihoods instead of calculating expensive jacobians on generative ODEs to calculate free energy differences and obtain the Boltzmann distribution.