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Semantic communication via VLM orchestrators unlocks robust, long-horizon coordination for multi-robot systems, outperforming prior single-robot approaches on complex manipulation tasks.
Smaller, cheaper models generate superior negative examples for preference distillation, significantly boosting student performance without requiring larger teacher models.
