Scaling Embodied Agents via Continued Pre-Training in Virtual Worlds

A study of 120 configurations shows how scaling data, diversity, and model size (up to 27B) affects embodied learning in diverse virtual environments. The research evaluates how continued pre-training (CPT) improves generalization across different visual styles and physical dynamics.