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. Setting this statement as the starting point of this work, it is proposed a model-agnostic training strategy that incorporates theory-guided data augmentation to simulate the effects of Lorentz transformations on jet data. The study starts with focusing on the state-of-the-art baseline ParticleNet, a neural network architecture designed for the direct processing of particle clouds for jet tagging. To evaluate the effectiveness of the proposed approach, several experiments are conducted with different augmentation strategies and assess the performance of the augmented models on the widely used top-tagging and quark-gluon reference datasets.