AgentGen: Automating Environment and Task Generation to Enhance Planning Abilities
Practical Solutions and Value
Large Language Models (LLMs) have revolutionized artificial intelligence, especially in agent-based systems. However, a major challenge is the labor-intensive process of creating diverse planning environments and tasks. AGENTGEN, developed by researchers at the University of Hong Kong and Microsoft Corporation, addresses this challenge by automating the generation of environments and planning tasks for LLM-based agents.
By utilizing LLMs, AGENTGEN generates a wide variety of environments and planning tasks, enriching the training datasets for LLM-based agents. This automation process involves two stages: environment generation and task generation, resulting in diverse and challenging scenarios for agent training.
The framework employs a bidirectional evolution method to create a comprehensive set of planning tasks, supporting a gradual and effective learning curve for the LLMs. AGENTGEN has been rigorously evaluated and has demonstrated significant improvements in the planning abilities of LLM-based agents across various models and tasks.
Overall, AGENTGEN offers a scalable, efficient approach to improving agent performance, addressing the limitations of manual design, and revolutionizing the training and application of LLM-based agents.
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