DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models

摘要

High-quality and diverse multimodal data are essential for improving vision–language models (VLMs), yet existing datasets often contain noisy, redundant, and poorly aligned samples. To address these problems, data filtering is commonly used to enhance the efficiency and performance of multimodal learning, but it introduces extra computational cost because filtering models are usually trained on the same data they are meant to screen. To reduce this cost, we study DOSE, which explores whether off-the-shelf pretrained models that have never seen the target data can be used to select training samples for larger and stronger multimodal models without any task-specific training. Even without fine-tuning, these models can effectively assess text quality and image–text alignment to guide data selection. Based on this, we build a joint quality–alignment distribution and apply adaptive weighted sampling to select informative samples while maintaining long-tail diversity. This approach greatly enhances data diversity and enables models trained on DOSE-filtered data to achieve comparable or even better results than those trained on the full dataset in standard VQA and math benchmarks. Extensive experiments demonstrate the effectiveness, efficiency, and scalability of our method.