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Video World Models Need Predictable Latents

This paper proposes making the latent space of video world models balance predictability with pixel reconstruction accuracy, achieved through temporal objectives for representation learning paired with a past-conditioned decoder architecture. Under the same training budget, inference cost is reduced by up to 5x, training speed increases by more than 7x, and pixel and perceptual errors in out-of-distribution and long-horizon prediction drop by 15-24%. The authors argue that temporal predictability should be an important design principle for video world model latent spaces and provide a concrete method for incorporating it into autoencoder training.

Part of the event: Video World Models Propose Predictable Latent Space Training Method

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