TADS: Task-Aware Data Selection for Multi-Task Multimodal Pre-Training

• URL: http://arxiv.org/abs/2602.05251v1
• Date: Thu, 05 Feb 2026 03:08:45 GMT
• Title: TADS: Task-Aware Data Selection for Multi-Task Multimodal Pre-Training
• Authors: Guanjie Cheng, Boyi Li, Lingyu Sun, Mengying Zhu, Yangyang Wu, Xinkui Zhao, Shuiguang Deng,
• Abstract summary: We introduce TADS (Task-Aware Data Selection), a novel framework for multi-task multimodal pre-training.<n> TADS integrates Intrinsic Quality, Task Relevance, and Distributional Diversity into a learnable value function.<n>A feedback-driven meta-learning mechanism adaptively refines the selection strategy based on proxy model performance.
• Score: 29.962039479618543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale multimodal pre-trained models like CLIP rely heavily on high-quality training data, yet raw web-crawled datasets are often noisy, misaligned, and redundant, leading to inefficient training and suboptimal generalization. Existing data selection methods are either heuristic-based, suffering from bias and limited diversity, or data-driven but task-agnostic, failing to optimize for multi-task scenarios. To address these gaps, we introduce TADS (Task-Aware Data Selection), a novel framework for multi-task multimodal pre-training that integrates Intrinsic Quality, Task Relevance, and Distributional Diversity into a learnable value function. TADS employs a comprehensive quality assessment system with unimodal and cross-modal operators, quantifies task relevance via interpretable similarity vectors, and optimizes diversity through cluster-based weighting. A feedback-driven meta-learning mechanism adaptively refines the selection strategy based on proxy model performance across multiple downstream tasks. Experiments on CC12M demonstrate that TADS achieves superior zero-shot performance on benchmarks like ImageNet, CIFAR-100, MS-COCO, and Flickr30K, using only 36% of the data while outperforming baselines by an average of 1.0%. This highlights that TADS significantly enhances data efficiency by curating a high-utility subset that yields a much higher performance ceiling within the same computational constraints.

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