Summary of Agentgym: Evolving Large Language Model-based Agents Across Diverse Environments, by Zhiheng Xi et al.
AgentGym: Evolving Large Language Model-based Agents across Diverse Environments
by Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Dingwen Yang, Chenyang Liao, Xin Guo, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a step towards building generally-capable agents using large language models (LLMs). The goal is to create agents that can handle diverse tasks and evolve themselves across different environments. Current approaches either require human supervision or limit environmental exploration, resulting in specialist agents with limited generalization. To address this, the authors identify three key ingredients: diverse environments for agent learning, a trajectory set to equip agents with basic capabilities, and an effective evolution method. They propose AgentGym, a framework featuring various environments and tasks for broad agent exploration, as well as a novel method, AgentEvol, to investigate agent self-evolution beyond previously seen data. Experimental results show that evolved agents can achieve comparable results to state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating AI agents that can do many different things and learn from new experiences. The authors are trying to make these agents better by giving them more information and a way to improve themselves. They’re proposing a new framework called AgentGym, which has lots of environments and tasks for the agents to explore and learn from. They also have a new method called AgentEvol that helps the agents learn from new experiences. The results show that these evolved agents are just as good as the best ones already out there. |
Keywords
» Artificial intelligence » Generalization
