Retrospective In-Context Learning for Temporal Credit Assignment with Large Language Models

One-line Summary

The study introduces a method using large language models for more efficient temporal credit assignment in reinforcement learning, achieving better sample efficiency and generalization.

Plain-language Overview

Training self-evolving agents, like robots or AI systems, often involves learning from sparse feedback, which can be inefficient. This paper presents a novel approach that uses large language models to convert sparse feedback into more informative signals, which helps the agents learn more effectively. This method, called Retrospective In-Context Learning (RICL), improves how agents assign credit to their actions over time, making the learning process more efficient. The results show that this approach matches traditional methods in performance while using fewer samples, indicating a promising direction for future AI development.

Technical Details