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Post-Training Frontier Text-to-Image Models by Composing Preference and Rubric Rewards
Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because no single reward signal captures the full range of human preference. In this work, we develop a simple and effective post-training recipe for open-doma
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