Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds

The paper investigates whether scaling data, diversity, and model capacity benefits embodied agents in virtual worlds. Using 120 training setups across four model sizes, six data budgets, and five data mixtures, the authors find that more data improves in‑corpus performance, diversity aids transfer to related worlds, and larger models boost data efficiency but may generalize less to distant worlds. They also identify distinct training phases and fit a scaling law, concluding that continued pre‑training alone is insufficient for fully generalist agents.

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• agents
• robotics
• benchmarks