Justin Cui

Scaling visual preference optimization hinges on data quality, as demonstrated by the finding that standard DPO suffices for a sufficiently large and clean dataset, while a novel Poly-DPO objective is crucial for noisy data.

LLMs can guide dense retrieval far more effectively by actively exploring the embedding space with Gaussian Processes, outperforming standard reranking even with a limited LLM budget.