A selector trained once on LLaVA-665K in CLIP space selects 15% of instructions to reach 98.3% of full-data performance and generalizes to an unseen dataset and different VLMs.
Coido: Efficient data selection for visual instruction tuning via coupled importance-diversity optimization,
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Once-For-All: A Train-Once and Select-Anytime Framework for Multimodal Instruction Tuning
A selector trained once on LLaVA-665K in CLIP space selects 15% of instructions to reach 98.3% of full-data performance and generalizes to an unseen dataset and different VLMs.