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This paper introduces the Thinking Reward Model (TRM) that explicitly formulates case-adaptive rubrics for evaluating visual generation, achieving state-of-the-art performance among open-source reward models.
The article explores whether AI aids human thinking or merely helps avoid it, highlighting the risk of relying on AI for comfortable answers instead of engaging in deeper contemplation.