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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.13979 |
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| _version_ | 1866911111226327040 |
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| author | Yang, Yi Ikemura, Kei Zhang, Qingwen Zhu, Xiaomeng Li, Ci Batool, Nazre Mansouri, Sina Sharif Folkesson, John |
| author_facet | Yang, Yi Ikemura, Kei Zhang, Qingwen Zhu, Xiaomeng Li, Ci Batool, Nazre Mansouri, Sina Sharif Folkesson, John |
| contents | Recent multi-task learning studies suggest that linear scalarization, when using well-chosen fixed task weights, can achieve comparable to or even better performance than complex multi-task optimization (MTO) methods. It remains unclear why certain weights yield optimal performance and how to determine these weights without relying on exhaustive hyperparameter search. This paper establishes a direct connection between linear scalarization and MTO methods, revealing through extensive experiments that well-performing scalarization weights exhibit specific trends in key MTO metrics, such as high gradient magnitude similarity. Building on this insight, we introduce AutoScale, a simple yet effective two-phase framework that uses these MTO metrics to guide weight selection for linear scalarization, without expensive weight search. AutoScale consistently shows superior performance with high efficiency across diverse datasets including a new large-scale benchmark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Yang, Yi Ikemura, Kei Zhang, Qingwen Zhu, Xiaomeng Li, Ci Batool, Nazre Mansouri, Sina Sharif Folkesson, John Machine Learning Recent multi-task learning studies suggest that linear scalarization, when using well-chosen fixed task weights, can achieve comparable to or even better performance than complex multi-task optimization (MTO) methods. It remains unclear why certain weights yield optimal performance and how to determine these weights without relying on exhaustive hyperparameter search. This paper establishes a direct connection between linear scalarization and MTO methods, revealing through extensive experiments that well-performing scalarization weights exhibit specific trends in key MTO metrics, such as high gradient magnitude similarity. Building on this insight, we introduce AutoScale, a simple yet effective two-phase framework that uses these MTO metrics to guide weight selection for linear scalarization, without expensive weight search. AutoScale consistently shows superior performance with high efficiency across diverse datasets including a new large-scale benchmark. |
| title | AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.13979 |