Sequential decision-making problems typically require significant human supervision and data.

Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model.

LLMs excel at few-shot in-context learning (ICL) - learning from a few input-output examples ("shots") provided in context at inference, without any weight updates.