LeWAM: End-to-End World and Action Modeling with JEPAs

Minghao Fu, Tavis Siebert, Eryk Halicki et al.1 min readHugging FaceOct 8, 2026

Key Takeaway for Students

Joint-embedding predictive architectures (JEPAs) learn compact latent world models without pixel reconstruction. Existing JEPA pipelines typically train visual representations and action-conditioned dynamics before adding a control policy or solver. We introduce LeWAM, an end-to-end JEPA for control with: (1) a joint training objective over next latent state prediction and acti

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This research significantly advances the efficiency and effectiveness of AI agents in complex, real-world tasks by improving planning and control. For ML engineers, understanding these end-to-end architectures is crucial for developing robust and performant autonomous systems.

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Career Insight

high demand

Why it matters

Skills to learn

Implement Joint-Embedding Predictive Architectures (JEPAs)Develop action-conditioned dynamics modelsDesign and evaluate control policies for robotic tasksOptimize latent space representations for planning efficiencyIntegrate visual perception with reinforcement learning agents

Interview questions

Explain the core differences between LeWAM's end-to-end training objective and traditional JEPA pipelines for control. How does this impact performance?
Describe how the isotropic Gaussian constraint on the latent state in LeWAM contributes to both direct policy execution and efficient planning. What are the trade-offs?
Design a system architecture for deploying a LeWAM-based agent in a real-world robotic manipulation task, considering visual input, action generation, and real-time constraints.

Relevant for ~24 months · generated by the enrichment pipeline