💡 The paper introduces LeWorldModel, a stable end to end joint embedding predictive architecture that trains efficiently from raw pixels. Existing methods for learning world models in compact latent spaces are fragile and rely on complex loss terms, pre trained encoders, or auxiliary supervision to avoid representation collapse. LeWorldModel addresses this issue by using only two loss terms, a next embedding prediction loss and a regularizer, to train the model end to end from raw pixels. This approach reduces the number of tunable loss hyperparameters from six to one compared to existing methods. The model has approximately 15 million parameters and can be trained on a single GPU in a few hours, making it up to 48 times faster than foundation model based world models. The results show that LeWorldModel remains competitive across diverse 2D and 3D control tasks and encodes meaningful physical structures in its latent space. The model is also able to reliably detect physically implausible events, demonstrating its ability to learn a robust and generalizable representation of the world. Overall, LeWorldModel provides a stable and efficient framework for learning world models from raw pixels, making it a significant contribution to the field of artificial intelligence.