MLLM-CL: Continual Learning for Multimodal Large Language Models

• URL: http://arxiv.org/abs/2506.05453v2
• Date: Wed, 01 Oct 2025 01:41:15 GMT
• Title: MLLM-CL: Continual Learning for Multimodal Large Language Models
• Authors: Hongbo Zhao, Fei Zhu, Haiyang Guo, Meng Wang, Rundong Wang, Gaofeng Meng, Zhaoxiang Zhang,
• Abstract summary: We introduce MLLM-CL, a novel benchmark encompassing domain and ability continual learning.<n>We propose preventing catastrophic interference through parameter isolation and an MLLM-based routing mechanism.<n>Our approach can integrate domain-specific knowledge and functional abilities with minimal forgetting, significantly outperforming existing methods.
• Score: 39.19456474036905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent Multimodal Large Language Models (MLLMs) excel in vision-language understanding but face challenges in adapting to dynamic real-world scenarios that require continuous integration of new knowledge and skills. While continual learning (CL) offers a potential solution, existing benchmarks and methods suffer from critical limitations. In this paper, we introduce MLLM-CL, a novel benchmark encompassing domain and ability continual learning, where the former focuses on independently and identically distributed (IID) evaluation across evolving mainstream domains, whereas the latter evaluates on non-IID scenarios with new model abilities. Methodologically, we propose preventing catastrophic interference through parameter isolation and an MLLM-based routing mechanism. Extensive experiments demonstrate that our approach can integrate domain-specific knowledge and functional abilities with minimal forgetting, significantly outperforming existing methods. Our benchmark and code are available at https://github.com/bjzhb666/MLLM-CL.

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