CALM: Consensus-Aware Localized Merging for Multi-Task Learning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909650135285760 |
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| author | Yan, Kunda Zhang, Min Cui, Sen Qu, Zikun Jiang, Bo Liu, Feng Zhang, Changshui |
| author_facet | Yan, Kunda Zhang, Min Cui, Sen Qu, Zikun Jiang, Bo Liu, Feng Zhang, Changshui |
| contents | Model merging aims to integrate the strengths of multiple fine-tuned models into a unified model while preserving task-specific capabilities. Existing methods, represented by task arithmetic, are typically classified into global- and local-aware methods. However, global-aware methods inevitably cause parameter interference, while local-aware methods struggle to maintain the effectiveness of task-specific details in the merged model. To address these limitations, we propose a Consensus-Aware Localized Merging (CALM) method which incorporates localized information aligned with global task consensus, ensuring its effectiveness post-merging. CALM consists of three key components: (1) class-balanced entropy minimization sampling, providing a more flexible and reliable way to leverage unsupervised data; (2) an efficient-aware framework, selecting a small set of tasks for sequential merging with high scalability; (3) a consensus-aware mask optimization, aligning localized binary masks with global task consensus and merging them conflict-free. Experiments demonstrate the superiority and robustness of our CALM, significantly outperforming existing methods and achieving performance close to traditional MTL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13406 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CALM: Consensus-Aware Localized Merging for Multi-Task Learning Yan, Kunda Zhang, Min Cui, Sen Qu, Zikun Jiang, Bo Liu, Feng Zhang, Changshui Machine Learning Artificial Intelligence Model merging aims to integrate the strengths of multiple fine-tuned models into a unified model while preserving task-specific capabilities. Existing methods, represented by task arithmetic, are typically classified into global- and local-aware methods. However, global-aware methods inevitably cause parameter interference, while local-aware methods struggle to maintain the effectiveness of task-specific details in the merged model. To address these limitations, we propose a Consensus-Aware Localized Merging (CALM) method which incorporates localized information aligned with global task consensus, ensuring its effectiveness post-merging. CALM consists of three key components: (1) class-balanced entropy minimization sampling, providing a more flexible and reliable way to leverage unsupervised data; (2) an efficient-aware framework, selecting a small set of tasks for sequential merging with high scalability; (3) a consensus-aware mask optimization, aligning localized binary masks with global task consensus and merging them conflict-free. Experiments demonstrate the superiority and robustness of our CALM, significantly outperforming existing methods and achieving performance close to traditional MTL. |
| title | CALM: Consensus-Aware Localized Merging for Multi-Task Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.13406 |