We proposed a reinforcement learning-based training framework for grounding GUI agents. Utilizing high-quality seed data filtering, dense policy reward, and an attention-based self-evolution mechanism significantly improves the ability to locate UI elements. With only 3k training samples, SE-GUI-7B model achieves state-of-the-art performance among models of the same scale on multiple benchmark tasks.