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World models can now self-improve by identifying their own prediction errors, thanks to a clever decomposition of action-conditioned prediction into easier-to-verify components.
Q-functions and implicit policy extraction are game-changers for batch online RL in robotics, unlocking significant performance gains over imitation-based approaches.
