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VPFlow argues that value prediction, not accurate state prediction, is what offline planning requires, training latent world models on closed-form Monte Carlo returns and planning in noise space via evolutionary search with a one-step flow policy. It surpasses model-free baselines by 30.3 and 10.1 percentage points on state and pixel OGBench benchmarks while outperforming latent planners like TD-MPC2.