RationalRewards recovers rationales from preference data via PARROT to create a critique-first reward model that improves visual generators at both training time through RL and test time through prompt refinement, matching RL fine-tuning performance while using far less data.
To do this, you must first assess the image on four critical aspects, provide justifications and absolute scores in 1--4 scale
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RationalRewards: Reasoning Rewards Scale Visual Generation Both Training and Test Time
RationalRewards recovers rationales from preference data via PARROT to create a critique-first reward model that improves visual generators at both training time through RL and test time through prompt refinement, matching RL fine-tuning performance while using far less data.