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Hierarchical JEPAs unlock efficient long-horizon visual planning by learning distinct latent spaces for different temporal scales, dramatically boosting success rates and reducing compute.
Cross-modal prediction in MJEPA not only boosts performance but also reveals that a unified encoder can outperform traditional modality-specific approaches by leveraging shared information.
