In the basic meta-learning set-up, one learning system adjusts the operations of a second learning system, such that the latter operates with improved speed and efficiency. The “lower-level” system learns quickly and adapts to each new task, and the slower “higher-level” system works to tune the lower-level system across tasks, which share underlying regularities.
Meta-learning was first introduced in Harlow’s monkey experiment.