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SVP-IL boosts success rates on ambiguous language tasks by over 60% with minimal training data, revolutionizing data efficiency in robotic manipulation.
CUAs can achieve a 73.7% success rate on complex macOS tasks, but the secret to their performance lies in skill libraries, not just framework design.
Decomposing GUI agent trajectories into verifiable milestones and auditing the evidence chain yields a 10% boost in RL training performance, outperforming single-judge reward systems.
