RL² capability enables fast reinforcement learning via slow outer-loop training

AI Impact Summary

RL² introduces a meta-learning approach where a slow outer-loop reinforcement learner guides rapid adaptation in the fast inner loop. This can dramatically improve sample efficiency and generalization across tasks for RL-powered products. Implementing this capability will require changes to training pipelines to support nested optimization, longer-running experiments, and robust monitoring of convergence and policy drift. Teams should anticipate additional compute and tooling needs to manage outer-loop updates alongside standard RL workloads.