Summary
This paper introduces rDPO, a new preference optimization framework designed to enhance Direct Preference Optimization (DPO) for fine-grained visual reasoning tasks. It addresses the limitation of existing DPO pipelines that rely on coarse preference data by proposing instance-specific, checklist-style rubrics. These rubrics define essential and additional criteria for each image-instruction pair, aiming to generate more effective preference data that better reflects nuanced quality differences.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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