Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs
This work addresses the problem of efficiently extending multimodal LLMs to new modalities for the AI research community, offering a simple and reusable solution that is incremental in nature.
The paper tackles performance degradation in modality-incremental continual learning for multimodal LLMs by identifying forgetting and misalignment issues, and proposes the MERA method, which achieves an average of 99.84% Backward Relative Gain across four modalities, resulting in nearly lossless performance.
Recent advances in Multimodal Large Language Models (MLLMs) have enhanced their versatility as they integrate a growing number of modalities. Considering the heavy cost of training MLLMs, it is efficient to reuse the existing ones and extend them to more modalities through Modality-incremental Continual Learning (MCL). The exploration of MCL is in its early stages. In this work, we dive into the causes of performance degradation in MCL. We uncover that it suffers not only from forgetting as in traditional continual learning, but also from misalignment between the modality-agnostic and modality-specific components. To this end, we propose an elegantly simple MCL paradigm called "MErge then ReAlign" (MERA) to address both forgetting and misalignment. MERA avoids introducing heavy model budgets or modifying model architectures, hence is easy to deploy and highly reusable in the MLLM community. Extensive experiments demonstrate the impressive performance of MERA, holding an average of 99.84\% Backward Relative Gain when extending to four modalities, achieving nearly lossless MCL performance. Our findings underscore the misalignment issue in MCL. More broadly, our work showcases how to adjust different components of MLLMs during continual learning.