The tension between physics-based and empirical CGMD model development is still being resolved, as it is in many adjacent fields. On the one hand, physics-based models are typically derived as approximations of (or inspired by) fundamental physical relationships, and tend to be relatively simple, interpretable, transferable, and comparatively less accurate. On the other hand, ML-based models typically dispense with physical priors, are purely empirical, highly complex, non-transferable, and comparatively more accurate. Examples of physics-based CGMD representations are the decomposition of the bonded contributions to the PES into stretching, bending and torsional interactions; the pair-wise decomposition of non-bonded contributions to the PES and the functional forms like Lennard-Jones, etc. used to approximate these interactions. Physics-based CGMD models have been parameterized to reproduce a variety of atomistic properties, including interbead forces and potential energy, , structural correlations, , and dynamics. ML-based CGMD models have almost exclusively been trained to reproduce atomistic energies and forces, and up to now have differed in their featurizations and specific architectures. The DeePCG model uses a beadwise local neighborhood descriptor and NN, , the initial CGnet model used handpicked features with a NN, which was later improved by replacing the curated features with filter convolutions. Wang et al. used an autoencoder to learn CG compressions that were then used by a NN to predict the PES. Scherer et al. used a nonlinear kernel to transform two-body and three-body interactions before applying simple linear regression to obtain forces , and John et al. used monomer-based features and Gaussian Process regression to model the PES . Less evident from this brief review are the many ways that physics-based and ML approaches can inform one another. Most ML force-fields already incorporate many physical constraints like rotational and translational invariance, particle indistinguishability, and physics-motivated ideas like particle-typing and particle-wise energy decompositions. Likewise, derivative-free optimization using algorithms like Particle Swarm Optimization have been used to parameterize CGMD models using physically-motivation functional forms to reproduce properties that cannot be analytically optimized using simpler algorithms. Here we explore a possible synthesis of these two approaches to CGMD modeling through what is known as difference learning (or ∆-learning in the electronic structure community, Fig. ). ∆-models are a type of transfer learning whereby a NN is used to learn the difference between an approximate model and the ground truth. The rationale for this approach is that only learning the physics that is missing from the approximate model is easier than trying to learn the CG physics from scratch. In scenarios where an informationrich and relatively general approximate model is available (as they are in CGMD) the ∆-model potentially inherits this trans-ferability, reduces the training data requirements of the ML model, and improves accuracy. To test this approach, we first benchmark a representative CGMD model (DeePCG) against a simple physics (SP)-based CGMD method (Iterative Boltzmann Inversion, IBI). Although this only done as a benchmark activity, this is remarkably the first head-to-head comparison that has been published of IBI and an ML-based CGMD model on the same target system(s). We then compare the performance of two ∆-models based on IBI with and without a pressure correction, respectively, against baseline MLonly and IBI-only models in reproducing the condensed phase atomistic behaviors of a molecular liquid using four distinct CG mappings (Fig. ) and a macromolecular liquid (Fig. ).