ICONS: Influence Consensus for Vision-Language Data Selection

• URL: http://arxiv.org/abs/2501.00654v3
• Date: Tue, 10 Jun 2025 18:19:52 GMT
• Title: ICONS: Influence Consensus for Vision-Language Data Selection
• Authors: Xindi Wu, Mengzhou Xia, Rulin Shao, Zhiwei Deng, Pang Wei Koh, Olga Russakovsky,
• Abstract summary: Training vision-language models via instruction often relies on large mixtures of data spanning diverse tasks and domains.<n>Existing methods typically rely on task-agnostics to estimate data importance or focus on optimizing single tasks in isolation.<n>We introduce ICONS, a gradient-based Influence CONsensus approach for vision-language data Selection.
• Score: 39.454024810266176
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training vision-language models via instruction tuning often relies on large mixtures of data spanning diverse tasks and domains. However, these mixtures frequently include redundant information, increasing computational costs without proportional performance gains, necessitating more effective data selection strategies. Existing methods typically rely on task-agnostic heuristics to estimate data importance or focus on optimizing single tasks in isolation, limiting their effectiveness in multitask settings. In this work, we introduce ICONS, a gradient-based Influence CONsensus approach for vision-language data Selection. Our method leverages first-order training dynamics to estimate the influence of individual training examples on validation performance and aggregates these estimates across tasks via majority voting over task-specific influences. This cross-task consensus identifies data points that are consistently valuable across tasks, enabling us to prioritize examples that drive overall performance. The voting-based design further mitigates issues such as score calibration and outlier sensitivity, resulting in robust and scalable data selection for diverse multitask mixtures. With only 20% of the data from LLaVA-665K and Cambrian-7M, our selected subsets retain 98.6% and 98.8% of the performance achieved with full datasets, and can even surpass full data training at a 60% selection ratio on LLaVA-665K. Our approach also generalizes to unseen tasks and architectures, demonstrating strong transfer. We release two compact, high-utility subsets, LLaVA-ICONS-133K and Cambrian-ICONS-1.4M, preserving impactful training examples for efficient and scalable vision-language model development.

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