  Critical slowing down and topological freezing severely hinder Monte Carlo
sampling of lattice field theories as the continuum limit is approached.
Recently, significant progress has been made in applying a class of generative
machine learning models, known as "flow-based" samplers, to combat these
issues. These generative samplers also enable promising practical improvements
in Monte Carlo sampling, such as fully parallelized configuration generation.
These proceedings review the progress towards this goal and future prospects of
the method.
