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AdaptRubric's innovative two-stage framework boosts GUI reward modeling performance by over 3.6 F1 points, showcasing the power of task-adaptive criteria.
Agents trained with SEE-generated trajectories not only navigate complex multi-step procedures but also achieve unprecedented task success rates in real-world applications.
Real-world GUI agents can achieve over 72% success in complex mobile tasks by leveraging a unique hybrid training approach that integrates real-device execution and adaptive learning from failures.
