In this article we have presented a conceptually simple procedure to incorporate datasets computed at different levels of theory into the training of a transferable MLFF. Due to the different interests and preferences of independent researchers, each new dataset that enters the public domain will contain complementary and incomplete data relative to what is needed to train a universal MLFF. These datasets will likely continue to be published utilizing different density functionals, basis sets and approximations. We believe that a method similar to what we have discussed in this article will be widely adopted when training foundational MLFFs in the future. We have demonstrated that one can transfer knowledge of different tasks, such as the description of the conformational energies of ions and intermolecular interactions, when adding datasets of different levels of theory. That is, the different datasets do not need to share the same chemical space.