  The recent introduction of Machine Learning techniques, especially
Normalizing Flows, for the sampling of lattice gauge theories has shed some
hope on improving the sampling efficiency of the traditional Hybrid Monte Carlo
(HMC) algorithm. In this work we study a modified HMC algorithm that draws on
the seminal work on trivializing flows by L\"uscher. Autocorrelations are
reduced by sampling from a simpler action that is related to the original
action by an invertible mapping realised through Normalizing Flows models with
a minimal set of training parameters. We test the algorithm in a $\phi^{4}$
theory in 2D where we observe reduced autocorrelation times compared with HMC,
and demonstrate that the training can be done at small unphysical volumes and
used in physical conditions. We also study the scaling of the algorithm towards
the continuum limit under various assumptions on the network architecture.
