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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.
Forget everything you thought you knew about continual learning: pretrained Vision-Language-Action models can learn new robotic skills without catastrophic forgetting, even with minimal replay.
