Not Every Turn Matters: Progressive Linearized Trajectory Matching for Multimodal Dataset Distillation

Multimodal Dataset Distillation (MDD) compresses image–text datasets into synthetic pairs that retain cross-modal retrieval utility. Existing trajectory-based methods use one-epoch expert updates as matching targets. However, each target direction is tied to a sampled intermediate epoch, so a severely compressed synthetic set must reproduce directions that change frequently across training. Across 20 independently trained experts, we observe substantial direction changes across epochs but a consistent cross-epoch trend among experts. We therefore propose Progressive Linearized Trajectory Matching (PLTM), which aligns every target with the overall direction from the selected start state to the end state and progressively increases the matched interval as the synthetic pairs improve. This redesign provides a direction-consistent expert target. Under paired hard supervision, online relational alignment and diversity regularization preserve relations among pairs and sample diversity without optimizing an additional soft similarity matrix. PLTM incurs negligible computational overhead and approximately doubles distillation efficiency. Experiments on Flickr-30K and MS-COCO show that our method consistently outperforms previous state-of-the-art MDD methods under paired hard supervision and optional soft similarity supervision. On MS-COCO with 500 synthetic pairs, PLTM improves IR@10/TR@10 by 7.8%/6.1% without a soft similarity matrix and by 4.2%/4.4% with soft supervision.