  We propose inverse renormalization group transformations to construct
approximate configurations for lattice volumes that have not yet been accessed
by supercomputers or large-scale simulations in the study of spin glasses.
Specifically, starting from lattices of volume $V=8^{3}$ in the case of the
three-dimensional Edwards-Anderson model we employ machine learning algorithms
to construct rescaled lattices up to $V'=128^{3}$, which we utilize to extract
two critical exponents. We conclude by discussing how to incorporate numerical
exactness within inverse renormalization group approaches of disordered
systems, thus opening up the opportunity to explore a sustainable and
energy-efficient generation of exact configurations for increasing lattice
volumes without the use of dedicated supercomputers.
