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A single-rollout RLVR method achieves faster training and more stable gradients by using a trajectory's own future as a hindsight context, eliminating the need for group sampling and reference solutions.
Current models struggle with hybrid interface tasks, achieving only a 41.2% success rate, underscoring a critical gap in CUA evaluation.
Achieve real-time, high-precision GUI navigation with minimal resources by pruning redundant visual tokens *without* retraining.