This episode discusses AGENTGEN, a framework that enhances the planning capabilities of LLM-based agents by automatically generating diverse environments and tasks for agent training. Traditionally, agent training relies on manually designed environments, limiting the variety and complexity of training scenarios. AGENTGEN overcomes this by using LLMs to generate environments based on diverse text segments and tasks that evolve in difficulty through a bidirectional evolution method (BI-EVOL).
Key Stages:
1. Environment Generation: LLMs create environment specifications, which are turned into code and added to a library for future use.
2. Task Generation: The system generates planning tasks with varying difficulty, either simplifying or complicating goals to support smoother learning.Evaluation shows AGENTGEN outperforms GPT-3.5, GPT-4, and Llama3 in a variety of tasks, demonstrating its ability to improve LLM-based agents' planning capabilities.
https://arxiv.org/pdf/2408.00764