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Forget hand-crafted reward functions: $\text{RLR}^3$ leverages rubrics and LLMs to provide fine-grained, multi-criteria supervision, outperforming standard RLVR in vision-language tasks.
Fine-grained rubrics unlock significantly better visual reasoning in preference optimization, rivaling GPT-5.4 with a much smaller model.
