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

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

Keywords: Deep Multi-Task Learning, Optimization for Deep Learning, Deep Reinforcement Learning

TL;DR: We show that a basic multi-task learning optimizer performs on par with specialized algorithms and suggest a possible explanation based on regularization.

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.

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:2201.04122/code)