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Decentralized robotics data can yield stronger manipulation policies than centralized training: slotting locally trained client LoRAs directly into a server MoE beats centralized PEFT on real hardware by 12.23% while slashing communication by 87%.
Algorithmic mode collapse in ARLs reveals a critical gap between in-loop metric gains and real-world performance, but DAPS can bridge this divide effectively.
