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arXiv · 2201.04122

In Defense of the Unitary Scalarization for Deep Multi-Task Learning

Vitaly Kurin·Alessandro De Palma·Ilya Kostrikov·Shimon Whiteson·M. Pawan Kumar
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Abstract

Recent multi-task learning research argues against unitary scalarization, where training simply minimizes the sum of the task losses. Several ad-hoc multi-task optimization algorithms have instead been proposed, inspired by various hypotheses about what makes multi-task settings difficult. The majority of these optimizers require per-task gradients, and introduce significant memory, runtime, and implementation overhead. We show that unitary scalarization, coupled with standard regularization and stabilization techniques from single-task learning, matches or improves upon the performance of complex multi-task optimizers in popular supervised and reinforcement learning settings. We then present an analysis suggesting that many specialized multi-task optimizers can be partly interpreted as forms of regularization, potentially explaining our surprising results. We believe our results call for a critical reevaluation of recent research in the area.

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Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, M. Pawan Kumar. 2022-01-11. In Defense of the Unitary Scalarization for Deep Multi-Task Learning. https://arxiv.org/abs/2201.04122

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