Title: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Mulitmodal representation learning, Interpretability, Representation fine-tuning

Abstract: Multimodal data in machine learning promises to improve generalization and performance on complex tasks. However, training multimodal models requires extensive paired datasets, can be computationally expensive, and lacks transparency by entangling shared and modality-specific signals in ways that hinder interpretability and control. In this work, we introduce MultiLoReFT: a low-rank representation fine-tuning framework for multimodal learning using pretrained unimodal models. Our approach extends low-rank representation finetuning to the multimodal setting and learns interpretable projection subspaces that decouple shared and modality-specific information. MultiLoReFT adaptively learns the rank of each subspace to best capture complementary contributions of each modality with minimal trainable parameters.  Our method offers an efficient and scalable solution to adapting pretrained representations for multimodal reasoning, enabling interpretable fine-tuning across both synthetic and real-world benchmarks.