  In many personalized recommendation scenarios, the generalization ability of
a target task can be improved via learning with additional auxiliary tasks
alongside this target task on a multi-task network. However, this method often
suffers from a serious optimization imbalance problem. On the one hand, one or
more auxiliary tasks might have a larger influence than the target task and
even dominate the network weights, resulting in worse recommendation accuracy
for the target task. On the other hand, the influence of one or more auxiliary
tasks might be too weak to assist the target task. More challenging is that
this imbalance dynamically changes throughout the training process and varies
across the parts of the same network. We propose a new method: MetaBalance to
balance auxiliary losses via directly manipulating their gradients w.r.t the
shared parameters in the multi-task network. Specifically, in each training
iteration and adaptively for each part of the network, the gradient of an
auxiliary loss is carefully reduced or enlarged to have a closer magnitude to
the gradient of the target loss, preventing auxiliary tasks from being so
strong that dominate the target task or too weak to help the target task.
Moreover, the proximity between the gradient magnitudes can be flexibly
adjusted to adapt MetaBalance to different scenarios. The experiments show that
our proposed method achieves a significant improvement of 8.34% in terms of
NDCG@10 upon the strongest baseline on two real-world datasets. The code of our
approach can be found at here: https://github.com/facebookresearch/MetaBalance
