Machine learning-based approaches, such as Boltzmann generators ; ; , learn surrogate models of the intractable Boltzmann distribution. If these surrogate models allow for exact sample likelihood estimation, highly effective sampling of unbiased equilibrium statistics can be achieved via reweighting or importance sampling. While a promising strategy, current architectural limitations in the deep neural networks used to learn such surrogate models have prevented their broad adoption.