Retrieval-Augmented LLM Agents: Learning to Learn from Experience

Thomas Palmeira Ferraz, Romain Deffayet, Vassilina Nikoulina, Hervé Déjean, Stéphane Clinchant

Preprint - Under Review

TL;DR: We investigate a strong baseline for LLM Agents Memory: retrieving experience from similar tasks. We show that LLM agents generalize better to never-before-seen tasks when they are trained not only to act, but also to use retrieved experience during training. We analyses key choices behind effective experience retrieval fine-tuning.

ARXIV BIB PDF POSTER

LLM Agents Retrieval Continual Learning