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A frozen world-model planner's performance can be drastically improved by adapting its latent rollouts to controller-induced execution shifts, rather than directly substituting predicted trajectories.
Autonomous driving gets a boost: CRAFT cleverly combines the best of both worlds – dense counterfactual supervision and grounded closed-loop feedback – to significantly improve driving policies.