The paper introduces LeWorldModel (LeWM), the first Joint-Embedding Predictive Architecture (JEPA) capable of stable, end-to-end training directly from raw pixels. Existing world models often rely on complex multi-term losses or pre-trained encoders to avoid representation collapse, but LeWM simplifies this process using a streamlined two-term objective.

• Simplified Training: LeWM uses a next-embedding prediction loss and a single regularizer called SIGReg, which enforces a Gaussian distribution on latent embeddings to prevent collapse. This reduces the number of effective tunable hyperparameters to just one, making it significantly easier to optimize than previous alternatives.
• Efficiency and Speed: With only 15M parameters, the model can be trained on a single GPU in a few hours. During inference, it performs latent planning up to 48× faster than world models based on large foundation models.
• Physical Understanding: Probing experiments demonstrate that LeWM’s latent space captures meaningful physical properties, such as object locations and angles. It also successfully detects "surprise" in physically implausible scenarios through a violation-of-expectation framework.

Performance and Evaluation

LeWM was evaluated across diverse 2D and 3D tasks, including navigation and robotic manipulation. It consistently outperformed or remained competitive with state-of-the-art baselines like PLDM and DINO-WM while offering superior training stability and faster planning speeds. Additionally, the researchers observed that latent trajectories in LeWM naturally become "straighter" over time—a phenomenon linked to improved temporal dynamics—without any explicit regularization.