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PerceptRL: Making Visual Perception Rewardable in MLLMs from Answers to Interactions
Fine-grained visual perception remains a bottleneck during post-training of multimodal large language models (MLLMs), as perceptual errors can constrain gains from reasoning-focused optimization. Although image-caption pre-training improves perception, its influence can weaken as subsequent stages c
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