Getting Space Right: Agentic Rewards for Image Generation
Reinforcement learning (RL) is promising for spatial text-to-image generation, but existing work largely focuses on policy optimization, assuming accurate rewards and effective online rollouts. We observe in-domain gains with out-of-domain degradation, exposing two limitations: unreliable spatial rewards in open-world...
Xiaofeng Tan, Jia-Xin Lin, Haobo Yuan et al.
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