MLLM-CL Benchmark Description

MLLM-CL is 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 emerging model ability. For more details, please refer to:

MLLM-CL: Continual Learning for Multimodal Large Language Models [paper], [HF paper], [code]. ‪Hongbo Zhao, Fei Zhu, Haiyang Guo, Meng Wang, Rundong Wang, ‪Gaofeng Meng, ‪Zhaoxiang Zhang‬

Usage

This repo is used to open-source all the experts in MLLM-CL experiments, including 4 branches (DCL_InternVL, DCL_LLaVA, ACL_InternVL, ACL_LLaVA).

Citation

@article{zhao2025mllm,
  title={MLLM-CL: Continual Learning for Multimodal Large Language Models},
  author={Zhao, Hongbo and Zhu, Fei and Guo, Haiyang and Wang, Meng and Wang, Rundong and Meng, Gaofeng and Zhang, Zhaoxiang},
  journal={arXiv preprint arXiv:2506.05453},
  year={2025}
}

Contact

Please post an issue on our GitHub.

About us: MLLM-CL Community

We are the members from MLLM-CL, an open-source community focused on Continual learning of Multimodal Large Language Models. If you are interested in our community, feel free to contact us on GitHub or by email.