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arXiv · 2508.13979

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics

Yi Yang·Kei Ikemura·Qingwen Zhang·Xiaomeng Zhu·Ci Li·Nazre Batool·Sina Sharif Mansouri·John Folkesson
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Abstract

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.

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Yi Yang, Kei Ikemura, Qingwen Zhang, Xiaomeng Zhu, Ci Li, Nazre Batool, Sina Sharif Mansouri, John Folkesson. 2025-08-19. AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics. https://arxiv.org/abs/2508.13979

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