To investigate the effect of data diversity in a kinetically aware model, we used active learning to sample ∼57k C, H, O, N, F, and Cl-containing acyclic small molecules (up to 15 heavy atoms) from PubChem. We then obtained their thermal degradation half-lives using Reaction Mechanism Generator (RMG 3.2.0), leveraging its extensive pyrolysis mechanism database extracted from both computational and experimental literature. However, the stability score model trained on this broader dataset of literature-derived kinetics performed similarly to the alkane model for small-molecule predictions and generally underperformed in predicting polymer stability, with only marginal improvements for polymers containing Cl and F. This result suggests that the mechanistic completeness of the kinetics in the alkane dataset may be more valuable for crossdomain generalization than the increased chemical diversity offered by the literature-derived-but mechanistically incomplete-data.