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This paper introduces a lightweight probe for multi-step gradients of pretrained weights that incurs no additional GPU memory cost and only marginal time overhead, and employs a spectrum-aware, importance-based rank allocation and optimal initialization derived from multi-step gradients.
This work proposes Snap2Review, a novel framework that harmonizes cross-modal retrieval with fine-grained preference learning, and introduces Tri-DPO, which employs a stratified negative sampling strategy with varying difficulty levels to force the model to discern fine-grained user preferences.
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