arXiv Machine Learning June 16, 2026 By Anke Tang, Xianglin Luo, Li Shen, Yong Luo, Liang Ding, Han Hu, Bo Du, Dacheng Tao

Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

fine-tuning

arXiv AI

Jun 18

Essential Subspace Merging for Multi-Task Learning

arXiv:2606. 19164v1 Announce Type: cross Abstract: Model merging aims to enable multi-task learning by integrating the capabilities of multiple models fine-tuned from the same pre-trained checkpoint into a single model.

By Longhua Li, Lei Qi, Xin Geng, Qi Tian

fine-tuning

arXiv Machine Learning

Jun 18

PACT: Preserving Anchored Cores in Task-vectors for Model Merging

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

fine-tuning benchmarks

arXiv Machine Learning

Jun 8

Closed-Form Spectral Regularization for Multi-Task Model Merging

arXiv:2606. 07289v1 Announce Type: new Abstract: Model merging combines several independently fine-tuned experts into a single multi-task model without any training data, reducing the storage, serving, and decentralized-development costs of large foundation models.

By Yongxian Wei, Runxi Cheng, Xingxuan Zhang, Li Shen, Chun Yuan, Peng Cui, Dacheng Tao

computer-vision fine-tuning multimodal benchmarks

Hugging Face Trending Papers

Jun 25

Learning to Recover Task Experts from a Multi-Task Merged Model

Multi-task model merging aims to consolidate several task-specific experts into a unified model, yet static merging consistently suffers from parameter interference. While dynamic merging models aim to bridge this gap, many works rely on the costly storage and loading of redundant expert components at inference.

nlp

arXiv Machine Learning

Aug 10

Training-free Task Classification for Multi-Task Model Merging

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

computer-vision nlp fine-tuning benchmarks

arXiv AI

Jun 26

Learning to Recover Task Experts from a Multi-Task Merged Model

arXiv:2606. 26902v1 Announce Type: new Abstract: Multi-task model merging aims to consolidate several task-specific experts into a unified model, yet static merging consistently suffers from parameter interference.

By Jinwook Jung, Taegyu Kim, Kumju Jo, Sungyong Baik

nlp

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Essential Subspace Merging for Multi-Task Learning

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Learning to Recover Task Experts from a Multi-Task Merged Model

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