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University of Central Florida
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Compressing multi-dimensional human preferences into single binary labels severely degrades DPO training, but a semi-supervised approach can recover state-of-the-art performance without additional human annotation.
Scaling visual preference optimization hinges on data quality, as a massive, high-resolution dataset renders complex optimization algorithms unnecessary.
