Hence, we importantly replace this manually-tuned and static mixing ratio with a 'dynamic' mixing ratio learning approach, where a controller automatically switches between the tasks during training, based on the current state of the multi-task model.
we use a multi-armed bandits based controller with Boltzmann exploration (Kaelbling et al., 1996) with an exponential moving average update rule.