AI

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

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 22/10/2026, 04:03:23 UTC.

Briefings

A 2026 study investigates whether scaling data, diversity, and model capacity in games improves embodied learning across diverse virtual worlds. The research tested 120 configurations across four model sizes (0.8B to 27B), six data budgets (50 to 1,600 hours), and five data mixtures. Results show that increased data improves in-corpus performance, diversity helps transfer to related worlds with limited gains in distant ones, and larger models improve data efficiency but generalize worse to distant worlds under single-world training. The study identifies an early interface phase with broad cross-world benefits followed by a later world phase with gains narrowed to in-corpus and related worlds. Models are competitive with Minecraft specialists in 3D worlds and perform best in real-time settings, though persistent gaps in distant worlds suggest the need for richer training stages beyond continued pre-training.

Key points

• The study tested 120 configurations across four model sizes (0.8B to 27B), six data budgets (50 to 1,600 hours), and five data mixtures.
• Results show that increased data improves in-corpus performance, diversity helps transfer to related worlds with limited gains in distant ones, and larger models improve data efficiency but generalize worse to distant worlds under single-world training.

Context

Embodied learning in physical environments shows gains from scaling data, diversity, and model capacity, but whether these gains extend to virtual worlds with different visual styles, dynamics, controls, and objectives remains unclear. This study investigates this question through a controlled study of continued pre-training (CPT) across three axes.

Limitations

The evidence is from a preprint (arXiv:2610.28698v1) and is limited to a single snapshot of the research. The study does not provide causal claims or opinions, and the findings are subject to future validation and potential gaps in understanding distant world generalization.

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