arXiv:2608. 12842v1 Announce Type: new Abstract: Model merging has recently attracted significant attention as a promising paradigm for constructing unified multi-task models without requiring additional retraining.
By Yuchen Liu, Zongzhen Yang, Binhang Qi, Hailong Sun, Xiang Gao
arXiv:2606. 22589v2 Announce Type: replace Abstract: Ever since the advent of foundation models and the pre-training-finetuning paradigm, there have been numerous efforts to merge multiple task-specific experts into a single multi-task model.
By Jungyong Son, Jinwook Jung, Sungyong Baik
arXiv:2606. 18627v1 Announce Type: new Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model.
By Ningyuan Shi, Zhipeng Zhou, Hao Wang, Chunyan Miao, Peilin Zhao
The paper introduces DiGA, a Disentangled Geometry-Aware framework for merging pretrained models. DiGA orthogonally decomposes each task vector into components tied to distinct geometric attributes, aggregates these components independently, and then recombines them, thereby preserving each component’s geometric identity. Experiments across various models, tasks, and merging methods show that DiGA improves merged-model performance and reduces capability degradation.
By Zijing Wang, Yongkang Liu, Mingyang Wang, Ercong Nie, Mengjie Zhao, Yunpu Ma, Kang Liu, Zihan Wang, Shi Feng, Daling Wang, Hinrich Sch\"utze
arXiv:2312. 06173v2 Announce Type: replace Abstract: Merging models fine-tuned from a common, extensively pre-trained large model but specialized for different tasks has been demonstrated as a cheap and scalable strategy to construct a multi-task model that performs well across diverse tasks.
By Anke Tang, Xianglin Luo, Li Shen, Yong Luo, Liang Ding, Han Hu, Bo Du, Dacheng Tao
The paper introduces a new multi‑task model‑merging framework that tackles task interference by projecting task vectors into a high‑dimensional sparse feature space using Sparse Autoencoders, enabling feature‑level disentanglement before fusion. It also proposes a lightweight Group‑Ranked Zeroth‑Order Optimizer to identify task‑critical layers for selective merging, reducing computational overhead. Experiments on Qwen2.5‑1.5B and Qwen2.5‑7B show consistent performance gains over several baselines across reasoning, code generation, instruction following, and general knowledge tasks, with a 2.78% improvement in a highly conflicting four‑task setting.
By Yihang Zhang, Shengke Sun, Junjie Wen, Feng Zeng