Efficient Sequential Decision Making with Large Language Models

Understand

This paper focuses on extending the success of large language models (LLMs) to sequential decision making.

• Existing efforts either (i) re-train or finetune LLMs for decision making, or (ii) design prompts for pretrained LLMs.
• The former approach suffers from the computational burden of gradient updates, and the latter approach does not show promising results.
• In this paper, we propose a new approach that leverages online model selection algorithms to efficiently incorporate LLMs agents into sequential decision making.