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TRQAM stabilizes off-policy reinforcement learning by precisely controlling deviations from pretrained policies, leading to a 68% success rate—22% higher than the best prior method.
RLDX-1 achieves double the success rate of existing VLAs on complex humanoid tasks, suggesting a leap in robots' ability to handle contact-rich, dynamic manipulation.
