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High-quality dense rewards can elevate robotic manipulation success rates from 50% to near perfection, transforming how robots learn from their environments.
Uncovering that CoT reasoning transitions from uncertainty to high reliability opens the door to more efficient inference strategies that can save computational resources without sacrificing accuracy.
Training VLA policies without human demos is now feasible, with LEGS achieving better performance than traditional methods at a fraction of the cost.
