We note that the cost-benefit proposition for using manybody ML potentials for coarse-graining is much worse than for classical atomistic simulations. In the atomistic case, the representability errors are much larger due to the coarsegraining of the electronic degrees of freedom upon adopting the classical frame. Conversely, CG potentials become simpler as they are coarse grained and are less likely to benefit from a ML-potential. The cases benchmarked here are relatively simple and do not involve phase-changes. Perhaps, in scenarios with complex aggregation or density differences an ML potential would show a stronger benefit. Likewise, it is possible that by supplying additional training objectives beyond the atomistic forces would justify additional model complexity. In such cases, we would expect ∆-learning models to still provide essentially free gains over their ML-only counterparts due to their negligible costs.