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Transforming unresolved failures into a powerful learning signal, FIRE-VLA reduces mean L2 error in autonomous driving models by nearly 19% while maintaining policy efficiency.
Reducing visual tokens doesn't always mean faster inference; a pre-vision strategy can significantly cut latency by bypassing preprocessing steps.
WorldScape Policy 2.0 achieves unprecedented long-horizon autonomous planning by integrating reasoning-augmented memory with multimodal instruction processing.
