@inproceedings{feng-etal-2025-aimmerging,
title = "{AIMM}erging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning",
author = "Feng, Yujie and
Li, Jian and
Dong, Xiaoyu and
Xu, Pengfei and
Zhou, Xiaohui and
Zhang, Yujia and
Lu, Zexin and
Wang, Yasha and
Zhao, Alan and
Chu, Xu and
Wu, Xiao-Ming",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.678/",
pages = "13431--13448",
ISBN = "979-8-89176-332-6",
abstract = "Continual learning (CL) is essential for deploying large language models (LLMs) in dynamic real-world environments without the need for costly retraining. Recent model merging-based methods have attracted significant attention, but they still struggle to effectively manage the trade-off between learning new knowledge and preventing forgetting, a challenge largely stemming from suboptimal number of merges and merging frequency. In this paper, we introduce Adaptive Iterative Model Merging (AimMerging), a novel CL framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model{'}s training status. Guided by dynamic monitoring, the training trajectory-guided merge controller adaptively determines the timing and frequency of iterative fusion, while the rehearsal-based knowledge fusion module computes the merging weights and executes the fusion. Comprehensive experiments on three CL benchmarks with various model sizes (from 770M to 13B) demonstrate that AimMerging achieves significant performance improvements over existing state-of-the-art methods, with an average relative improvement of 80{\%} and 59{\%} on FWT and BWT, respectively. The source code is provided for reproducibility."
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<abstract>Continual learning (CL) is essential for deploying large language models (LLMs) in dynamic real-world environments without the need for costly retraining. Recent model merging-based methods have attracted significant attention, but they still struggle to effectively manage the trade-off between learning new knowledge and preventing forgetting, a challenge largely stemming from suboptimal number of merges and merging frequency. In this paper, we introduce Adaptive Iterative Model Merging (AimMerging), a novel CL framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model’s training status. Guided by dynamic monitoring, the training trajectory-guided merge controller adaptively determines the timing and frequency of iterative fusion, while the rehearsal-based knowledge fusion module computes the merging weights and executes the fusion. Comprehensive experiments on three CL benchmarks with various model sizes (from 770M to 13B) demonstrate that AimMerging achieves significant performance improvements over existing state-of-the-art methods, with an average relative improvement of 80% and 59% on FWT and BWT, respectively. The source code is provided for reproducibility.</abstract>
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%0 Conference Proceedings
%T AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning
%A Feng, Yujie
%A Li, Jian
%A Dong, Xiaoyu
%A Xu, Pengfei
%A Zhou, Xiaohui
%A Zhang, Yujia
%A Lu, Zexin
%A Wang, Yasha
%A Zhao, Alan
%A Chu, Xu
%A Wu, Xiao-Ming
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F feng-etal-2025-aimmerging
%X Continual learning (CL) is essential for deploying large language models (LLMs) in dynamic real-world environments without the need for costly retraining. Recent model merging-based methods have attracted significant attention, but they still struggle to effectively manage the trade-off between learning new knowledge and preventing forgetting, a challenge largely stemming from suboptimal number of merges and merging frequency. In this paper, we introduce Adaptive Iterative Model Merging (AimMerging), a novel CL framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model’s training status. Guided by dynamic monitoring, the training trajectory-guided merge controller adaptively determines the timing and frequency of iterative fusion, while the rehearsal-based knowledge fusion module computes the merging weights and executes the fusion. Comprehensive experiments on three CL benchmarks with various model sizes (from 770M to 13B) demonstrate that AimMerging achieves significant performance improvements over existing state-of-the-art methods, with an average relative improvement of 80% and 59% on FWT and BWT, respectively. The source code is provided for reproducibility.
%U https://aclanthology.org/2025.emnlp-main.678/
%P 13431-13448
Markdown (Informal)
[AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning](https://aclanthology.org/2025.emnlp-main.678/) (Feng et al., EMNLP 2025)
ACL
- Yujie Feng, Jian Li, Xiaoyu Dong, Pengfei Xu, Xiaohui Zhou, Yujia Zhang, Zexin Lu, Yasha Wang, Alan Zhao, Xu Chu, and Xiao-Ming Wu. 2025. AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 13431–13448, Suzhou, China. Association for Computational Linguistics.