DynaSaur: Large Language Agents Beyond Predefined Actions

Dang Nguyen, Viet Dac Lai, Seunghyun Yoon, Ryan A. Rossi, Handong Zhao, Ruiyi Zhang, Puneet Mathur, Nedim Lipka, Yu Wang, Trung Bui, Franck Dernoncourt, Tianyi Zhou

Keywords: LLM, LLM agents

TL;DR: We propose a flexible LLM agent framework for open-ended environments, where it dynamically generates new actions when existing ones are insufficient. These actions accumulate over time for future reuse.

Abstract: Existing LLM agent systems typically select actions from a fixed and predefined set at every step. While effective in closed, narrowly scoped environments, this approach presents two major challenges for real-world, open-ended scenarios: (1) it significantly restricts the planning and acting capabilities of LLM agents, and (2) it requires substantial human effort to enumerate and implement all possible actions, which is impractical in complex environments with a vast number of potential actions. To address these limitations, we propose an LLM agent framework that enables the dynamic creation and composition of actions in an online manner. In this framework, the agent interacts with its environment by generating and executing programs written in a general-purpose programming language. Furthermore, generated actions are accumulated over time for future reuse. Our extensive experiments across multiple benchmarks demonstrate that this framework significantly improves flexibility and outperforms prior methods that rely on a fixed action set. Notably, it enables LLM agents to adapt and recover in scenarios where predefined actions are insufficient or fail due to unforeseen edge cases.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the COLM Code of Ethics on https://colmweb.org/CoE.html

Author Guide: I certify that this submission complies with the submission instructions as described on https://colmweb.org/AuthorGuide.html

Submission Number: 1573