Trust What You Match: Reliable Cross-Modal Coupling Matching for Multimodal Dataset Distillation

Multimodal dataset distillation (MDD) aims to compress large-scale corpora into compact synthetic data while preserving their downstream utility. However, existing methods typically match cross-modal couplings estimated from stochastic mini-batches, making supervision highly sensitive to batch composition and consequently impairing preservation of dataset-level multimodal structure. To address this limitation, we propose Reliable Cross-Modal Coupling Matching (ReCoup), which calibrates encoder-space couplings using fluctuations induced by random image–text pairings within each mini-batch. Accordingly, ReCoup assigns higher weights to couplings that are more reproducible across batches and thus better reflect full-dataset statistics. The resulting reliability-aware objective yields more coherent synthetic-data gradients and reduces conflicting optimization updates, while an auxiliary constraint further preserves the diagonal coupling profile in the projected shared space. Experiments on Flickr30K and MS-COCO show consistent improvements over state-of-the-art MDD methods across synthetic-set budgets. With 500 synthetic pairs, ReCoup improves mean Recall by 7.3 points on Flickr30K and 5.1 points on MS-COCO.