This paper introduces OmniVBench, a comprehensive benchmark and dataset for evaluating and training reference-to-video generation models that can generate videos with diverse and complex references. Practitioners can use OmniVBench to assess and improve the performance of their R2V models, which is essential for developing more versatile and general video generation capabilities.
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This paper develops a method to improve reasoning models by reducing the discrepancy between a stronger teacher and an on-policy student, which helps to prevent the student from learning the teacher's own flaws. Practitioners might care because it can lead to more accurate models that better represent the capability gap between teachers and students.