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Deep multitask networks, in which one neural network produces multiplepredictive outputs, can offer better speed and performance than theirsingle-task counterparts but are challenging to train properly. We present agradient normalization (GradNorm) algorithm that automatically balancestraining in deep multitask models by dynamically tuning gradient magnitudes. Weshow that for various network architectures, for both regression andclassification tasks, and on both synthetic and real datasets, GradNormimproves accuracy and reduces overfitting across multiple tasks when comparedto single-task networks, static baselines, and other adaptive multitask lossbalancing techniques. GradNorm also matches or surpasses the performance ofexhaustive grid search methods, despite only involving a single asymmetryhyperparameter $\alpha$. Thus, what was once a tedious search process thatincurred exponentially more compute for each task added can now be accomplishedwithin a few training runs, irrespective of the number of tasks. Ultimately, wewill demonstrate that gradient manipulation affords us great control over thetraining dynamics of multitask networks and may be one of the keys to unlockingthe potential of multitask learning.

GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks