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A unified benchmark reveals the trade-offs between pixel-wise accuracy and perceptual realism in state-of-the-art image super-resolution techniques.
Stochastic negative sampling in Direct Preference Optimization (DPO) dramatically boosts recommendation accuracy, suggesting that carefully curated "wrong" answers are key to preference learning.
