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AgentGen: Automating Environment and Task Generation to Enhance Planning Abilities in LLM-Based Agents with 592 Environments and 7,246 Trajectories https://t.co/HU9C6xODsk #AgentGen #AI #ArtificialIntelligence #LLM #AgentTraining #ai #news #llm #ml #research #ainews #innovati… https://t.co/sxEWr3oZXD
AgentGen enhances LLM-based agents' planning by automating diverse environment generation and progressively challenging tasks, improving performance over existing models. https://t.co/2ZKiVmZcX8
🚀Exciting new research: AGENTGEN - A novel approach to LLM-based agent training Brought to you by the team behind the widely acclaimed @WizardLM_AI Key points: • Environment Generation • BI-EVOL This paper introduces a novel approach to generate diverse environments and… https://t.co/hbFU806jew
AgentGen, a novel approach to training language model-based agents, automates the generation of diverse environments and planning tasks. Developed by the team behind WizardLM_AI, AgentGen uses large language models (LLMs) to synthesize environments from a variety of domain-specific texts. The system progressively generates tasks of varying difficulty, enhancing the planning abilities of LLM-based agents. The research, which examines properties like emergence and collective intelligence in artificial systems, introduces 592 environments and 7,246 trajectories, demonstrating improved performance over existing models. Key features include environment generation and BI-EVOL.