Given the limited out-of-distribution performance, it is critical to continue expanding available training data for ML approaches; but it is also not obvious when this is needed and how to do so. Incomplete or inconsistent data curation procedures will lead to a limited chemical scope and unequally distributed errors in the trained models, while completely enumerating or sampling the entire reaction space is unrealistic due to the great complexity of organic reaction space ). This dilemma makes defining tractable reaction subspaces a nontrivial task and remains largely unresolved by the field, as reaction datasets that are still routinely used have typically either been hand-curated or limited to small reactants where brute-force reaction enumeration is feasible. Bridging physics-based and ML-based methods through graphically defined reaction templates offers a promising alternative to relying solely on either approach. Reaction templates offer a structured foundation that can serve as valuable input for machine learning models, providing a systematic and interpretable framework for understanding reaction patterns. Additionally, ML approaches can thrive at identifying and generalizing across complex reaction patterns through comprehensive datasets that include multiple reactions with similar types of bond-breaking and bond-forming information, which is precisely what graph-based templates offer. These templates can also improve tractability in generating first-principles reaction datasets with specific graph constraints like saturation, delocalization, and atom types. Realizing this synergy between templates and ML based approaches requires a formal definition of a model reaction and a thorough assessment of the errors associated with using these model reactions as surrogates for activation barrier prediction.