Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models地理多样性优于数据量：零样本JEPA自监督驾驶世界模型跨域泛化研究
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Researchers trained JEPA-based self-supervised world models on nuPlan data from Pittsburgh, Boston, and Singapore, then evaluated zero-shot generalization on held-out Argoverse 2 scenarios from Miami and Austin.
Geographically diverse training reduced mean surprise score by 16.5% versus single-geography training at equal scale (0.228 vs 0.273 on 63,000 scenarios), demonstrating that diversity significantly improves cross-domain generalization.
Training on 200,000 scenarios from a single geography (3x more data) still produced higher surprise (0.264) than the geographically mixed 63K model, showing data volume alone cannot compensate for geographic diversity.