With the affordable (N 3 ) nonlocality inspired by the strongly interacting limit of DFT, -the use of MRF is very promising for the machine learning of the new generation of XC functionals. Specifically, we plan to construct MRF via machine learning of the fluctuation function of Eq. 41. In this equation, we have an exchange term that can be learned from the 'exact MRF data' obtained from the 'rm-MRF' procedure from the exact exchange energy density (Sec. 7.2). Then, in the correlation terms of the fluctuation function, we will distinguish the physics-based objects (e.g., the second term on the r.h.s. whose physics is described in Ref. and the UEG part of the third term on the r.h.s. of Eq. 41 ) from the objects that will be machine learned. For example, F of Eq. 41 will be machine learned from the standard DFT (Jacob's ladder features) and MRF-specific fully nonlocal features (e.g., S i (r) and a i of Eqs. 12 and 13, respectively).