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A two-stage distillation pipeline first trains the causal student to match the teacher’s trajectories under a chunk-wise causal attention mask, then applies Distribution Matching Distillation (DMD) with backward simulation to close the few-step quality gap.
The first multiplayer world model for highly dynamic environments governed by complex physical interactions is introduced, and how behavior changes with model and data scale is characterized, including the capabilities that emerge and the failure modes that persist.
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