EvoGrad: Automatically Evolving Gradient Aggregation Rules for Multi-Task Learning Using Genetic Programming

Multi-task learning (MTL) aims to improve data efficiency and generalization by leveraging shared information across tasks. Many existing gradient balancing methods developed for MTL limit their aggregation rules (namely programs) to a linear search space spanned by task gradients, and determine the optimal programs merely based on a local decrease in training loss, which may not lead to model improvement. In this paper, we propose EvoGrad, an evolutionary framework based on multi-objective genetic programming (GP) that automatically discovers optimal programs. By composing nonlinear operations over task gradients, EvoGrad simultaneously explores more expressive search spaces beyond their linear span and identifies optimal programs. To accelerate the exploration, we adopt a hybrid selection strategy that first efficiently selects diverse and balanced candidate programs based on loss-decrease rates, and then identifies the best program using dataset-specific evaluation metrics. Experiments on four benchmark datasets with task numbers ranging from 2 to 40, together with three toy examples, demonstrate the effectiveness of EvoGrad in MTL.