Saved in:
| Main Authors: | Wang, Zhixiang, Mao, Zhenyu, Qiao, Yixuan, Wu, Yunfang, Li, Biye |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.12217 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging
by: Ju, Yiming, et al.
Published: (2024)
by: Ju, Yiming, et al.
Published: (2024)
Training-free LLM Merging for Multi-task Learning
by: Fu, Zichuan, et al.
Published: (2025)
by: Fu, Zichuan, et al.
Published: (2025)
Model Merging for Knowledge Editing
by: Fu, Zichuan, et al.
Published: (2025)
by: Fu, Zichuan, et al.
Published: (2025)
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging
by: Hui, Tingfeng, et al.
Published: (2024)
by: Hui, Tingfeng, et al.
Published: (2024)
Unraveling LoRA Interference: Orthogonal Subspaces for Robust Model Merging
by: Zhang, Haobo, et al.
Published: (2025)
by: Zhang, Haobo, et al.
Published: (2025)
Channel Merging: Preserving Specialization for Merged Experts
by: Zhang, Mingyang, et al.
Published: (2024)
by: Zhang, Mingyang, et al.
Published: (2024)
Merge to Mix: Mixing Datasets via Model Merging
by: Tao, Zhixu Silvia, et al.
Published: (2025)
by: Tao, Zhixu Silvia, et al.
Published: (2025)
Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging
by: Lu, Zhenyi, et al.
Published: (2024)
by: Lu, Zhenyi, et al.
Published: (2024)
Arcee's MergeKit: A Toolkit for Merging Large Language Models
by: Goddard, Charles, et al.
Published: (2024)
by: Goddard, Charles, et al.
Published: (2024)
Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging
by: Yang, Jinluan, et al.
Published: (2025)
by: Yang, Jinluan, et al.
Published: (2025)
Resolving Interference (RI): Disentangling Models for Improved Model Merging
by: Ramesh, Pratik, et al.
Published: (2026)
by: Ramesh, Pratik, et al.
Published: (2026)
Merge-of-Thought Distillation
by: Shen, Zhanming, et al.
Published: (2025)
by: Shen, Zhanming, et al.
Published: (2025)
K-Merge: Online Continual Merging of Adapters for On-device Large Language Models
by: Shenaj, Donald, et al.
Published: (2025)
by: Shenaj, Donald, et al.
Published: (2025)
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy
by: Li, Pingzhi, et al.
Published: (2023)
by: Li, Pingzhi, et al.
Published: (2023)
Merging in a Bottle: Differentiable Adaptive Merging (DAM) and the Path from Averaging to Automation
by: Gauthier-Caron, Thomas, et al.
Published: (2024)
by: Gauthier-Caron, Thomas, et al.
Published: (2024)
Dynamic Model Merging Made Slim
by: Du, Guodong, et al.
Published: (2026)
by: Du, Guodong, et al.
Published: (2026)
What Matters for Model Merging at Scale?
by: Yadav, Prateek, et al.
Published: (2024)
by: Yadav, Prateek, et al.
Published: (2024)
Model Assembly Learning with Heterogeneous Layer Weight Merging
by: Zhang, Yi-Kai, et al.
Published: (2025)
by: Zhang, Yi-Kai, et al.
Published: (2025)
STAR: Spectral Truncation and Rescale for Model Merging
by: Lee, Yu-Ang, et al.
Published: (2025)
by: Lee, Yu-Ang, et al.
Published: (2025)
Fisher Mask Nodes for Language Model Merging
by: K, Thennal D, et al.
Published: (2024)
by: K, Thennal D, et al.
Published: (2024)
Model Merging by Uncertainty-Based Gradient Matching
by: Daheim, Nico, et al.
Published: (2023)
by: Daheim, Nico, et al.
Published: (2023)
Multiplex Thinking: Reasoning via Token-wise Branch-and-Merge
by: Tang, Yao, et al.
Published: (2026)
by: Tang, Yao, et al.
Published: (2026)
OptiMer: Optimal Distribution Vector Merging Is Better than Data Mixing for Continual Pre-Training
by: Song, Haiyue, et al.
Published: (2026)
by: Song, Haiyue, et al.
Published: (2026)
Merging Text Transformer Models from Different Initializations
by: Verma, Neha, et al.
Published: (2024)
by: Verma, Neha, et al.
Published: (2024)
Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing
by: Lai, Kunfeng, et al.
Published: (2025)
by: Lai, Kunfeng, et al.
Published: (2025)
Branch-Solve-Merge Improves Large Language Model Evaluation and Generation
by: Saha, Swarnadeep, et al.
Published: (2023)
by: Saha, Swarnadeep, et al.
Published: (2023)
REAM: Merging Improves Pruning of Experts in LLMs
by: Jha, Saurav, et al.
Published: (2026)
by: Jha, Saurav, et al.
Published: (2026)
Grow Up and Merge: Scaling Strategies for Efficient Language Adaptation
by: Glocker, Kevin, et al.
Published: (2025)
by: Glocker, Kevin, et al.
Published: (2025)
Parameter Competition Balancing for Model Merging
by: Du, Guodong, et al.
Published: (2024)
by: Du, Guodong, et al.
Published: (2024)
Merging LoRAs like Playing LEGO: Pushing the Modularity of LoRA to Extremes Through Rank-Wise Clustering
by: Zhao, Ziyu, et al.
Published: (2024)
by: Zhao, Ziyu, et al.
Published: (2024)
Data-driven Clustering and Merging of Adapters for On-device Large Language Models
by: Bohdal, Ondrej, et al.
Published: (2026)
by: Bohdal, Ondrej, et al.
Published: (2026)
Model Merging and Safety Alignment: One Bad Model Spoils the Bunch
by: Hammoud, Hasan Abed Al Kader, et al.
Published: (2024)
by: Hammoud, Hasan Abed Al Kader, et al.
Published: (2024)
EvoMerge: Neuroevolution for Large Language Models
by: Jiang, Yushu
Published: (2024)
by: Jiang, Yushu
Published: (2024)
Quantifying and Mitigating Self-Preference Bias of LLM Judges
by: Yang, Jinming, et al.
Published: (2026)
by: Yang, Jinming, et al.
Published: (2026)
LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging
by: Lee, Seungeon, et al.
Published: (2025)
by: Lee, Seungeon, et al.
Published: (2025)
Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs
by: Kadhe, Swanand Ravindra, et al.
Published: (2024)
by: Kadhe, Swanand Ravindra, et al.
Published: (2024)
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
by: Kallini, Julie, et al.
Published: (2024)
by: Kallini, Julie, et al.
Published: (2024)
Linear Model Merging Unlocks Simple and Scalable Multimodal Data Mixture Optimization
by: Berasi, Davide, et al.
Published: (2026)
by: Berasi, Davide, et al.
Published: (2026)
One Size Does Not Fit All: A Distribution-Aware Sparsification for More Precise Model Merging
by: Luo, Yingfeng, et al.
Published: (2025)
by: Luo, Yingfeng, et al.
Published: (2025)
SPEAR-MM: Selective Parameter Evaluation and Restoration via Model Merging for Efficient Financial LLM Adaptation
by: Kapusuzoglu, Berkcan, et al.
Published: (2025)
by: Kapusuzoglu, Berkcan, et al.
Published: (2025)
Similar Items
-
Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging
by: Ju, Yiming, et al.
Published: (2024) -
Training-free LLM Merging for Multi-task Learning
by: Fu, Zichuan, et al.
Published: (2025) -
Model Merging for Knowledge Editing
by: Fu, Zichuan, et al.
Published: (2025) -
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging
by: Hui, Tingfeng, et al.
Published: (2024) -
Unraveling LoRA Interference: Orthogonal Subspaces for Robust Model Merging
by: Zhang, Haobo, et al.
Published: (2025)