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

• URL: http://arxiv.org/abs/2201.04122v1
• Date: Tue, 11 Jan 2022 18:44:17 GMT
• Title: In Defense of the Unitary Scalarization for Deep Multi-Task Learning
• Authors: Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, M. Pawan Kumar
• Abstract summary: We present a theoretical analysis suggesting that many specialized multi-tasks can be interpreted as forms of regularization. We show that, when coupled with standard regularization and stabilization techniques, unitary scalarization matches or improves upon the performance of complex multitasks.
• Score: 121.76421174107463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 present a theoretical analysis suggesting that many specialized multi-task optimizers can be interpreted as forms of regularization. Moreover, we show that, when coupled with standard regularization and stabilization techniques from single-task learning, unitary scalarization matches or improves upon the performance of complex multi-task optimizers in both supervised and reinforcement learning settings. We believe our results call for a critical reevaluation of recent research in the area.

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