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Current VLA models fail at zero-shot robotic personalization, highlighting a critical gap in embodied AI's ability to adapt to user preferences.
Quality-aware pretraining strategies, particularly approach-aware episode conditioning, can significantly outperform plain behavioral cloning in VLA tasks, with optimal choices depending on label granularity and model architecture.
The hardest AI tasks remain largely unsolved, with current models achieving only a 2.6% success rate on economically valuable workflows.