Sample-Efficient Learning from Agent Experience

Researchers introduced Experience Distillation, a way to compress useful agent interaction history into model weights without more environment interaction.

On software-engineering tasks and text-adventure games, it kept much of the gain from in-context learning.

It also outperformed direct fine-tuning on the same experience.

The approach could make agent learning more sample-efficient than standard reinforcement learning.