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Data mixing, especially with instruction-heavy data, emerges as the crucial factor for optimizing VLM training, challenging traditional filtering approaches.
Forget specialized classifiers鈥擫MMs, enhanced with in-context learning and a novel iterative refinement method (CIRCLE), can outperform even fine-tuned VLMs in both closed and open-world classification.