The nudged elastic band (NEB) method is commonly used for this, but it is computationally expensive as it requires optimizing a series of images along the reaction coordinate. This expense often leads to neglecting the possibility of multiple adsorbate or transition state (TS) structures for a given reaction, making it difficult to unambiguously identify the lowest energy saddle point. Given the exponential dependence of the rate coefficient on the DFT-derived barrier height, this can result in rate coefficients which are orders of magnitude too high or low. New approaches are needed to lower computational costs and avoid approximations which limit the application of MKMs. Machine learning potentials (MLPs) can accelerate the exploration of the potential energy surface by predicting forces at a fraction of the DFT cost, with accuracy approaching DFT when trained on sufficiently large DFT databases. Two promising strategies to speed up saddle-point searches with MLPs are (1) active learning to create system specific models and (2) using large pre-trained models. Active learning has significantly sped up NEB calculations using Behler-Parrinello neural networks (BPNN), but has not yet been applied to large reaction sets needed for MKMs. Another approach demonstrated active learning of a Gaussian approximation potential (GAP) model to accelerate NEB calculations and to run dynamic studies to bypass the common harmonic approximation. However, GAP models costs can scale unfavorably with system size, limiting its use for large reaction networks. Large DFT datasets like OC20 have enabled the development of general and accurate pre-trained models. Wander et al. introduced CatTSunami, a framework which leverages OC20-trained 38 models to accelerate comprehensive TS exploration using NEBs, achieving impressive speedups across various surfaces and reaction types. However, performance declines for more complex reactions (e.g., desorptions versus dissociations and transfer reactions). Follow-up work demonstrated that using the Hessian-based optimizer Sella improves the convergence of transition states explored with CatTSunami, though some reactions still pose challenges. While promising, pre-trained models require fine-tuning for broader use in systems studied with different DFT parameters compared to those used in generating the model's training dataset. While fine-tuning has been demonstrated on adsorbates, this has not yet been demonstrated for transition state optimization. Larger models also carry much higher computational cost per inference, which can limit their application in dynamical studies to directly obtain free energy barriers beyond the harmonic approximation. This work demonstrates an automated framework that can leverage both smaller models (trained through active learning) and large pre-trained models (via fine-tuning) for accelerating transition state optimization. Currently, to the best of our knowledge, there does not exist an automated, general, and parallelized workflow for training system-specific models for accelerating transition state searches across the large sets of reactions required for MKM development. In this work, this was achieved by integrating Pynta 47 (https: //github.com/zadorlab/pynta), an automated kinetics workflow tool for surface reactions, within a training curriculum for machine learning potentials based on neural network architectures. Specifically, we leverage Pynta's comprehensive transition state exploration to automatically sample the potential energy surface along the reaction coordinate regions for several reactions in parallel. We apply commonly used ensemble-based techniques to quantify model uncertainty, facilitating sampling of a set of the Pynta-generated configurations to iteratively construct a DFT dataset for training an rMLP. Tools for automatically determining valid transition states and generating the thermochemical and kinetic databases for updating RMG are now provided within the Pynta workflow.