Enhancing Interactive Agents with Large Language Models: The SwiftSage Framework
This article reviews the challenges of textual‑only large language model interaction, introduces benchmark environments such as AFL World and ScienceWorld, compares baseline reinforcement‑learning approaches, and presents SwiftSage—a hybrid system that combines a fast T5‑based small model with a powerful LLM for planning and grounding, demonstrating superior performance, efficiency, and cost‑effectiveness while outlining current limitations and future research directions.