Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning
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. However, existing methods primarily focus on average performance across all tasks and do not adequately address how to construct models accommodating different deployment environments or varying user preferences. This paper proposes a model merging framework, termed Tunable MAGMAX, which enables preference-aware control of task-specific performance in CL. Our method...
| Authors | Kei Hiroshima, Kento Uchida, Shinichi Shirakawa |
|---|---|
| Primary category | cs.LG |
| Published | 20 May 2026 |
| Last updated at source | 22 July 2026 |
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