While our most substantial changes for Sage were the LJ parameter refits, updates to our valence parameter training data and fitting procedures also substantially changed the force field, even apart from the LJ refits. In particular, valence parameter optimization resulted in improvements in global metrics of quality for generated conformers-RMSD and TFD-on benchmarks relative to previous generations of force fields, and in the case of relative conformer energetics (ΔΔ𝐸), a closer match to the previous best, which is Parsley 1.2.0. There has been significant refinement in the choice of training targets used in training our force fields since Parsley 1.0.0. These improvements have included capping the number of optimized conformers used as well as excluding vibrational frequency targets from training. For Sage, we also substantially expanded our quantum chemical benchmarking dataset, giving us a better view into changes in performance. This larger benchmark set, "OpenFF Industry Benchmark Season 1 v1.1", is an important first step in finding discrepancies in describing pharmaceutically relevant chemistries, although it does not currently cover all the valence parameters because some parameters are used only by rare chemistries that are uncommon in pharmaceutical datasets.