Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Platform and Content Group, Tencent（腾讯平台与内容事业群）；UCAS（中国科学院大学）；Sun Yat-sen University（中山大学）
Kunfeng Lai, Zhenheng Tang, Xinglin Pan, Peijie Dong, Xiang Liu, Haolan Chen, Huacan Wang, Li Shen, Bo Li, Xiaowen Chu
备注：
work in progress. arXiv admin note: text overlap with arXiv:2405.09673 by other authors
Model merging aggregates Large Language Models (LLMs) finetuned on different tasks into a stronger one. However, parameter conflicts between models leads to performance degradation in averaging. While model routing addresses this issue by selecting individual models during inference, it imposes excessive storage and compute costs, and fails to leverage the common knowledge from different models. In this work, we observe that different layers exhibit varying levels of parameter conflicts. Building on this insight, we average layers with minimal parameter conflicts and use a novel task-level expert routing for layers with significant conflicts. To further reduce storage costs, inspired by task arithmetic sparsity, we decouple multiple fine-tuned experts into a dense expert and several sparse experts. Considering the out-of-distribution samples, we select and merge appropriate experts based on the task uncertainty of the input data. We conduct extensive experiments on both LLaMA and Qwen with varying parameter scales, and evaluate on real-world reasoning tasks. Results demonstrate that our method consistently achieves significant performance improvements while requiring less system cost.
CoMerge: Conflict-Driven Preference Optimization for Multi-Task Model Merging
School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；HiThink Research（海思思考研究院）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；Zhejiang University（浙江大学）
Bridging Domains through Subspace-Aware Model Merging
Universidade Estadual de Campinas (UNICAMP)（科林斯州立大学）；Recod.ai Lab.（Recod.ai实验室）；Instituto de Computação（计算研究所）；CISPA Helmholtz Center for Information Security（信息安全赫尔姆霍茨中心）；Intercom
City University of Hong Kong（香港城市大学）；Tencent Jarvis Lab（腾讯 Jarvis 实验室）；University of Technology Sydney（悉尼大学）；University of Queensland（昆士兰大学）；Jilin University（吉林大学）；Westlake University（西湖大学）
PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，中国科学院自动化研究所）；Pengcheng Laboratory（鹏城实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Data Science and Artificial Intelligence Research Institute, China United Network Communications Group Co., Ltd.（中国联合网络通信集团有限公司数据科学与人工智能研究院）
ResMerge: Residual-based Spectral Merging of Large Language Models
Southeast University（东南大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）；Wuhan University of Technology（武汉理工大学）；Peking University（北京大学）；Wuhan AI Research（武汉人工智能研究院）
Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling
Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；Xiamen University Malaysia（厦门大学马来西亚分校）；Nanyang Technological University（南洋理工大学）；Beijing Academy of Artificial Intelligence, China（北京人工智能研究院）；Peng Cheng Laboratory, China（鹏城实验室）；Shaqra University, Saudi Arabia（沙迦大学）
Conflict-Free Replicated Data Types for Neural Network Model Merging: A Two-Layer Architecture Enabling CRDT-Compliant Model Merging Across 26 Strategies
AdaMerging: Adaptive Model Merging for Multi-Task Learning
Northeastern University（东北大学）；University of Oxford（牛津大学）；Nanyang Technological University（南洋理工大学）；University of Maryland（马里兰大学）；JD Explore Academy（京东探索研究院）
Towards Efficient Multimodal Unified Reasoning Model via Model Merging
Beijing University of Posts and Telecommunications（北京邮电大学）；Nanyang Technological University（南洋理工大学）；Beijing Key Laboratory of Network System and Network Culture（北京网络系统与网络文化重点实验室）；Key Laboratory of Interactive Technology and Experience System, Ministry of Culture and Tourism（文化和旅游部交互技术与体验系统重点实验室）；Zhongguancun Academy, Beijing, China（中关村学院，北京，中国）
Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens
AIML, University of Adelaide（AIML，阿德莱德大学）；Monash University（墨尔本大学）；Australian National University（澳大利亚国立大学）；Tiktok, Australia（Tiktok，澳大利亚）；Zhejiang University（浙江大学）
Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging
Zhejiang University（浙江大学）；Simon Fraser University（西蒙菲莎大学）；The Chinese University of Hong Kong（香港中文大学）；Zhejiang Key Lab of Accessible Perception and Intelligent Systems（浙江省可感知智能系统重点实验室）
Geometry Conflict: Explaining and Controlling Forgetting in LLM Continual Post-Training
The Hong Kong Polytechnic University, PolyU（香港理工大学，PolyU）；The Hong Kong University of Science and Technology, HKUST（香港科学与技术大学，HKUST）；PolyU-Daya Bay Technology and Innovation Research Institute（PolyU-大亚湾技术与创新研究院）
Auto-FlexSwitch: Efficient Dynamic Model Merging via Learnable Task Vector Compression
School of Mathematics, Harbin Institute of Technology, Harbin, P. R. China（哈尔滨工业大学数学学院，哈尔滨，中华人民共和国）；Shanghai Artificial Intelligence Laboratory, Shanghai, P. R. China（上海人工智能实验室，上海，中华人民共和国）
