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World-Model-Guided Navigation Distills Foresight into Fast Policy

Researchers propose a training-time approach for human-like navigation in multi-agent scenes, using world models as preference oracles to distill foresight into a fast policy. Two task-aligned world models score candidate actions by predicted consequences, and the policy is refined via group-relative preference distillation. At deployment, the world models are removed, enabling single forward-pass action selection. Evaluations across five scenarios show safer, more socially coherent, and more human-like navigation than baselines.