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Real-world robot policies can learn autonomously from sparse rewards by treating value predictions as task-progress memory, outperforming offline training by $32.8\%$ success rate.
Learning to predict the visual consequences of actions significantly boosts policy performance and generalization, while simultaneously improving world model accuracy.