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University of Electronic Science and Technology of China
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GOAL not only optimizes advertising incentives but also adapts seamlessly to varying ROI constraints without the need for retraining, revolutionizing how we approach incentivized user engagement.
Native computer use can be achieved at scale, enabling agents to outperform leading systems while significantly enhancing security against adversarial attacks.
MTP acceptance rates can be dramatically improved by addressing entropy fluctuations, leading to up to 1.8x faster RL training.
