Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning
The paper presents Tunable MAGMAX, a model merging framework designed for continual learning (CL) that accommodates user preferences. It introduces a preference vector to control task-specific performance during model merging, allowing for adaptability in various deployment environments. Experimental results indicate that Tunable MAGMAX achieves comparable or superior performance to existing methods, making it a practical solution for diverse CL applications.
- ▪Tunable MAGMAX enables preference-aware control of task-specific performance in continual learning.
- ▪The framework introduces a preference vector that adjusts the number of elements selected from each task vector during merging.
- ▪Experimental results demonstrate that Tunable MAGMAX effectively adapts merged models to various target environments.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20803 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Machine Learning arXiv:2605.20803 (cs) [Submitted on 20 May 2026] Title:Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning Authors:Kei Hiroshima, Kento Uchida, Shinichi Shirakawa View a PDF of the paper titled Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning, by Kei Hiroshima and 2 other authors View PDF HTML (experimental) Abstract:Continual learning (CL) aims to train models sequentially on multiple tasks while mitigating catastrophic forgetting of previously learned knowledge. Recent advances in large pre-trained models (LPMs) and model merging techniques, such as MAGMAX, have demonstrated effective CL performance by combining task-specific parameters.
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