Authors: Mengkang Hu, Pu Zhao, Can Xu, Qingfeng Sun, Jianguang Lou, Qingwei Lin, Ping Luo, Saravan Rajmohan, Dongmei Zhang
Abstract: Large Language Model (LLM) based agents have garnered significant attention
and are becoming increasingly popular. Furthermore, planning ability is a
crucial component of an LLM-based agent, involving interaction with the
environment and executing actions to complete a planning task, which generally
entails achieving a desired goal from an initial state. This paper investigates
enhancing the planning abilities of LLMs through instruction tuning, referred
to as agent training. Recent studies have demonstrated that utilizing
expert-level trajectory for instruction-tuning LLMs effectively enhances their
planning capabilities. However, existing work primarily focuses on synthesizing
trajectories from manually designed planning tasks and environments. The
labor-intensive nature of creating these environments and tasks impedes the
generation of sufficiently varied and extensive trajectories. To address this
limitation, this paper explores the automated synthesis of diverse environments
and a gradual range of planning tasks, from easy to difficult. We introduce a
framework, AgentGen, that leverages LLMs first to generate environments and
subsequently generate planning tasks conditioned on these environments.
Specifically, to improve environmental diversity, we propose using an
inspiration corpus composed of various domain-specific text segments as the
context for synthesizing environments. Moreover, to increase the difficulty
diversity of generated planning tasks, we propose a bidirectional evolution
method, Bi-Evol, that evolves planning tasks from easier and harder directions
to synthesize a task set with a smoother difficulty curve. The evaluation
results derived from AgentBoard show that AgentGen greatly improves LLMs’
planning ability, e.g., the AgentGen instruction-tuned Llama-3 8B surpasses
GPT-3.5 in overall performance. Moreover, in certain tasks, it even outperforms
GPT-4.
Source: http://arxiv.org/abs/2408.00764v1
