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Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
Not provided
embodied agentspre-traininggeneralizationvirtual worlds
2610.28698
Builder Relevance
2h ago70%
Abstract
The study investigates the impact of data volume, diversity, and model size on the performance of embodied agents in virtual worlds.
Reality Card
Core Claim
More data improves in-corpus performance, while diversity enhances transfer to related worlds, but model size can hinder generalization to distant worlds under single-world training.
Method / Result
120 training configurations were tested across four model sizes and six data budgets.
Limitations
The study suggests that building generalist embodied agents requires richer training stages beyond continued pre-training, indicating potential gaps in generalization.
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