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This paper demonstrates that small probe-based language models can replace larger generative models for rubric-based reinforcement learning rewards, enhancing efficiency while preserving agreement and transferability.
Introduces a rubric-based reinforcement learning method to improve visual faithfulness in vision-language models by decomposing responses into atomic propositions and scoring them on visual faithfulness, reasoning consistency, and instruction following.