Training vision-language models via instruction tuning relies on large data mixtures spanning diverse tasks and domains, yet these mixtures frequently include redundant information that increases computational costs without proportional gains. Existing methods typically rely on task-agnostic heuristics to estimate data importance, limiting their effectiveness across tasks. We introduce ICONS, a gradient-based Influence CONsensus approach for vision-language data Selection. Our method leverages first-order training dynamics to estimate each example's influence on validation performance, then aggregates these estimates across tasks via majority voting. This cross-task consensus identifies consistently valuable data points while mitigating score calibration and outlier sensitivity, enabling robust and scalable data selection for diverse multitask mixtures. Models trained on our selected 20% data subset from LLAVA-665K (respectively: from CAMBRIAN-7M, from VISION-FLAN-186K) retain 98.6% (respectively: 98.8%, 99.8%) of full-dataset performance. We demonstrate that our selected data generalizes to unseen tasks and model architectures, and release three compact subsets LLAVA-ICONS-133K, CAMBRIAN-ICONS-1.4M, and VISION-FLAN-ICONS-37K for efficient vision-language model development.
@article{arxiv.2501.00654,
title = {ICONS: Influence Consensus for Vision-Language Data Selection},
author = {Xindi Wu and Mengzhou Xia and Rulin Shao and Zhiwei Deng and Pang Wei Koh and Olga Russakovsky},
journal= {arXiv preprint arXiv:2501.00654},
year = {2025}
}