Cohere Labs - Chang Shi, PhD student, UT Austin

Learning from unlabeled video has emerged as a powerful paradigm for training world models without action supervision. However, existing approaches often rely on monolithic inverse and forward dynamics models, which struggle to scale in settings where different entities act simultaneously. In this work, we propose a factored dynamics framework FLAM that decomposes the latent state into in- dependent factors, each with its own inverse and forward model.

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