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    <abstract>Multimodal Continual Instruction Tuning (MCIT) empowers Multimodal Large Language Models (MLLMs) to adapt to ever-evolving requirements without continuous costly retraining. However, MCIT faces challenges in mitigating Catastrophic Forgetting (CF) and enhancing Knowledge Transfer (KT). Existing works combine Mixture-of-Expert (MoE) and LoRA to address these. However, using a fixed number of shared LoRA blocks across tasks can lead to the overwriting of acquired knowledge, making MLLMs harder to handle CF and KT. Therefore, we propose the **Prog**ressive **LoRA** framework (ProgLoRA), which contains a progressive LoRA pool and trains a new LoRA block for each incremental task to reduce knowledge interference. Specifically, ProgLoRA has two key mechanisms: task-aware allocation for effectively leveraging acquired knowledge at current task and task recall for realigning the model with learned tasks. Additionally, considering different application scenarios, we design a static ProgLoRA for the more idealized basic setting and a dynamic ProgLoRA for the more realistic challenging setting. Experiments on the latest MCIT benchmark demonstrate that ProgLoRA outperforms existing approaches.</abstract>
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%0 Conference Proceedings
%T Progressive LoRA for Multimodal Continual Instruction Tuning
%A Yu, Yahan
%A Zhang, Duzhen
%A Ren, Yong
%A Zhao, Xuanle
%A Chen, Xiuyi
%A Chu, Chenhui
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F yu-etal-2025-progressive
%X Multimodal Continual Instruction Tuning (MCIT) empowers Multimodal Large Language Models (MLLMs) to adapt to ever-evolving requirements without continuous costly retraining. However, MCIT faces challenges in mitigating Catastrophic Forgetting (CF) and enhancing Knowledge Transfer (KT). Existing works combine Mixture-of-Expert (MoE) and LoRA to address these. However, using a fixed number of shared LoRA blocks across tasks can lead to the overwriting of acquired knowledge, making MLLMs harder to handle CF and KT. Therefore, we propose the **Prog**ressive **LoRA** framework (ProgLoRA), which contains a progressive LoRA pool and trains a new LoRA block for each incremental task to reduce knowledge interference. Specifically, ProgLoRA has two key mechanisms: task-aware allocation for effectively leveraging acquired knowledge at current task and task recall for realigning the model with learned tasks. Additionally, considering different application scenarios, we design a static ProgLoRA for the more idealized basic setting and a dynamic ProgLoRA for the more realistic challenging setting. Experiments on the latest MCIT benchmark demonstrate that ProgLoRA outperforms existing approaches.
%R 10.18653/v1/2025.findings-acl.143
%U https://aclanthology.org/2025.findings-acl.143/
%U https://doi.org/10.18653/v1/2025.findings-acl.143
%P 2779-2796

Markdown (Informal)

[Progressive LoRA for Multimodal Continual Instruction Tuning](https://aclanthology.org/2025.findings-acl.143/) (Yu et al., Findings 2025)

• Progressive LoRA for Multimodal Continual Instruction Tuning (Yu et al., Findings 2025)

ACL

• Yahan Yu, Duzhen Zhang, Yong Ren, Xuanle Zhao, Xiuyi Chen, and Chenhui Chu. 2025. Progressive LoRA for Multimodal Continual Instruction Tuning. In Findings of the Association for Computational Linguistics: ACL 2025, pages 2779–2796, Vienna, Austria. Association for Computational Linguistics.