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This work adds one feature-alignment term to ordinary VLA training: a frozen world model is run over the training frames once and cached, and the student learns to agree with that cache, indicating a broad representational prior rather than a fragile alignment between two particular networks.
Compact diffusion models can now leverage the power of high-capacity Teachers without architectural changes, achieving remarkable performance gains.
