Before revision:  In addition to the challenges of offline RL , a unique distribution shift is present in meta RL: agents learn exploration strategies that can gather the experience needed to learn a new task , and also learn adaptation strategies that work well when presented with the trajectories in the dataset, but the adaptation strategies are not adapted to the data distribution that the learned exploration strategies collect.
After revision:  In addition to the challenges of offline RL , a unique distribution shift is present in meta RL: agents learn exploration strategies that can gather the experience needed to learn a new task . Since this policy was meta-trained on a fixed, offline dataset, it might behave unpredictably when adapting to data collected by the learned exploration strategies collect.