arXiv:2603.19312v3 Announce Type: replace Abstract: Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on complex multi-term losses, exponential moving averages, pre-trained encoders, or auxiliary supervision to avoid representation collapse. In this work, we introduce LeWorldModel (LeWM), the first JEPA that trains stably end-to-end from raw pixels using only two loss terms: a next-embedding prediction loss and a regularizer enforcing Gaussian-distributed latent embeddin
The continuous drive for more efficient and robust AI models, especially in 'world model' architectures, prompts innovations like LeWorldModel to address stability and complexity issues in current methods.
This development could significantly simplify and democratize the creation of advanced AI, making 'world models' more practical and accessible for sophisticated applications by reducing dependency on complex training regimes or pre-trained components.
AI research into comprehensive world models becomes more streamlined with a simpler, end-to-end stable architecture, potentially accelerating deployment and reducing barriers to entry for model development.
- · AI researchers and developers
- · Companies building autonomous systems
- · Cloud computing providers (for training)
- · Meta (if they are the primary developers)
- · Developers reliant on complex, multi-term loss functions
- · Systems requiring extensive pre-trained components
The simplification of 'world model' training could lead to faster development cycles for AI systems that understand and predict complex environments.
Reduced computational overhead and expertise requirements might accelerate the adoption of these models across various industries, from robotics to scientific simulation.
More robust and easily trainable world models could enable the creation of more sophisticated AI agents capable of higher levels of autonomy and decision-making in previously unmanageable scenarios.
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Read at arXiv cs.LG