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This paper addresses objective mismatch in model-based RL by proposing offline diagnostics to predict closed-loop performance of latent world models. On LunarLander-v3, the Reward Observability Fraction (ROF) and a Composite score (CROF) enable selecting checkpoints that yield strong MPC and model-based RL policies with far fewer real-environment interactions.