  In Changjun Fan et al. [Nature Communications
https://doi.org/10.1038/s41467-023-36363-w (2023)], the authors present a deep
reinforced learning approach to augment combinatorial optimization heuristics.
In particular, they present results for several spin glass ground state
problems, for which instances on non-planar networks are generally NP-hard, in
comparison with several Monte Carlo based methods, such as simulated annealing
(SA) or parallel tempering (PT). Indeed, those results demonstrate that the
reinforced learning improves the results over those obtained with SA or PT, or
at least allows for reduced runtimes for the heuristics before results of
comparable quality have been obtained relative to those other methods. To
facilitate the conclusion that their method is ''superior'', the authors pursue
two basic strategies: (1) A commercial GUROBI solver is called on to procure a
sample of exact ground states as a testbed to compare with, and (2) a
head-to-head comparison between the heuristics is given for a sample of larger
instances where exact ground states are hard to ascertain. Here, we put these
studies into a larger context, showing that the claimed superiority is at best
marginal for smaller samples and becomes essentially irrelevant with respect to
any sensible approximation of true ground states in the larger samples. For
example, this method becomes irrelevant as a means to determine stiffness
exponents $\theta$ in $d>2$, as mentioned by the authors, where the problem is
not only NP-hard but requires the subtraction of two almost equal ground-state
energies and systemic errors in each of $\approx 1\%$ found here are
unacceptable. This larger picture on the method arises from a straightforward
finite-size corrections study over the spin glass ensembles the authors employ,
using data that has been available for decades.
