Building general reading comprehension systems, capable of solving multiple datasets at the same time, is a recent aspirational goal in the research community.Prior work has focused on model architectures or generalization to held out datasets, and largely passed over the particulars of the multi-task learning set up.We show that a simple dynamic sampling strategy, selecting instances for training proportional to the multi-task model's current performance on a dataset relative to its singletask performance, gives substantive gains over prior multi-task sampling strategies, mitigating the catastrophic forgetting that is common in multi-task learning.We also demonstrate that allowing instances of different tasks to be interleaved as much as possible between each epoch and batch has a clear benefit in multitask performance over forcing task homogeneity at the epoch or batch level.Our final model shows greatly increased performance over the best model on ORB, a recently-released multitask reading comprehension benchmark.