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Vision-language-action models can achieve superior performance and drastically reduced latency by leveraging reasoning supervision without explicitly conditioning actions on generated reasoning.
Embodied AI agents can now continuously self-improve by co-evolving their policies, training curricula, and judges, overcoming the bottleneck of static evaluation.