In recent years, machine learning models for jet tagging in high-energy physics have gained considerable attention. However, many existing approaches overlook the physical invariants that jets must adhere to, particularly the fundamental spacetime symmetry governed by Lorentz transformations. In this study, we propose a model-agnostic training strategy incorporating theory-guided data augmentation to simulate the effects of Lorentz transformations on jet data. We specifically focus on the state-of-the-art baseline ParticleNet, a neural network architecture designed to process particle clouds for jet tagging directly. To evaluate the effectiveness of our approach, we conduct experiments with different augmentation strategies and assess the performance of the augmented models on the widely used top-tagging reference dataset. The results show that even a small application of the data augmentation strategy increases the robustness of the model to Lorentz boost attacks, i.e., high transformation \(\beta \) . While the accuracy of the baseline model decreases rapidly with increasing intensity of the transformation \(\beta \) , the augmented models exhibit more stable performance. Remarkably, models that underwent a moderate level of augmentation demonstrated a statistically significant performance boost on transformations beyond those seen at train time. This finding highlights the potential of the data augmentation strategy in enhancing model accuracy while preserving the essential physical properties of the jets.