Multi-task Learning (MTL) (Caruana, 1997) is an inductive transfer mechanism which leverages information from related tasks to improve the primary model's generalization performance.
for stage-1, the bandit controller iteratively selects batches of data from different tasks during training to learn the approximate importance of each auxiliary task (Graves et al., 2017).