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Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
Continued pre-training across data scale, diversity, and model size improves embodied learning in virtual worlds but reveals limits in distant-world generalization, motivating richer training beyond imitation.
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