Before revision:  Offline meta-RL removes the need to continuously provide reward supervision because rewards must only be provided once when the offline dataset is generated. 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:  Offline meta-RL a practical tool for real-world use, offline meta-RL presents additional challenges beyond online meta-RL or standard offline RL settings. Meta-RL learns an exploration strategy that collects data for adapting, and also meta-trains a policy that quickly adapts to data from 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.