Children often learn how to behave in an unfamiliar environment by imitating adults. Imitation learning (IL) enables a learning agent to behave in an unknown environment by observing expert demonstrations.

LLMs' flexibility is largely due to an "in-context learning" effect that emerges when

Offline reinforcement learning (RL) has progressed with return-conditioned supervised learning (RCSL), but its lack of stitching ability remains a limitation.

However, support for decision-making is lacking.