  Recent applications of machine-learned normalizing flows to sampling in
lattice field theory suggest that such methods may be able to mitigate critical
slowing down and topological freezing. However, these demonstrations have been
at the scale of toy models, and it remains to be determined whether they can be
applied to state-of-the-art lattice quantum chromodynamics calculations.
Assessing the viability of sampling algorithms for lattice field theory at
scale has traditionally been accomplished using simple cost scaling laws, but
as we discuss in this work, their utility is limited for flow-based approaches.
We conclude that flow-based approaches to sampling are better thought of as a
broad family of algorithms with different scaling properties, and that
scalability must be assessed experimentally.
