Grounding Language in Robotic Dancing: The Emergence of Advanced Language Models

In this article, the authors explore the combination of language models and strategic reasoning to enable human-level play in the game of diplomacy. They propose a novel approach that allows agents to learn and improve their decision-making skills through experience, much like humans do. The proposed method, called "instruction-finetuned foundation models," combines the strengths of both language models and strategic reasoning to create more effective agents.
The authors explain that traditional machine learning approaches often struggle to achieve human-level performance in complex tasks like diplomacy, as they lack the ability to reason strategically and understand context. To address this limitation, the proposed method incorporates a "step-by-step" approach, where each step consists of accessing a screenshot of the current UI and a dynamically generated document detailing the functions of UI elements and the actions’ effects on the current UI page. The agents are also prompted to provide their observations of the current UI, articulate their thought process concerning the task and current observations, and execute actions by invoking available functions.
The authors demonstrate the effectiveness of their approach through experiments using a real-world webagent with plan-ning, long context understanding, and program synthesis. The results show that the proposed method significantly outperforms existing approaches in terms of both efficiency and accuracy.
In summary, this article presents a novel approach to creating agents that can perform complex tasks like diplomacy at a human level. By combining language models and strategic reasoning, the proposed method enables agents to learn and improve their decision-making skills through experience, making them more effective in real-world applications.