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About this paper
Guanting Dong, Junting Lu, Junjie Huang, Wanjun Zhong, Longxiang Liu, Shijue Huang, Zhenyu Li, Yang Zhao and 12 more
Agent-World builds executable, stateful tool-and-database environments and uses fresh, verifiable tasks to diagnose agent failures and direct further reinforcement learning. Its ecosystem contains 1,978 retained environments and 19,822 tools across 20 first-tier and 50 second-tier categories. After two rounds, MCP-Mark (Post.) rose from 29.5 to 38.1 for Agent-World-14B, but the experiment does not isolate diagnosis from added training or data.
Key insights
- Unifying scalable real-world environment synthesis with continuous self-evolution provides a robust framework for improving general agent intelligence, addressing limitations of static training.
- Generating a diverse ecosystem of database-grounded executable environments and verifiable tasks from real-world sources is crucial for training agents capable of handling complex, stateful interactions.
- A closed-loop self-evolving training mechanism, where agents diagnose their weaknesses and drive targeted environment/task expansion, enables continuous, lifelong agent learning and co-evolution with the training environments.
Summary by alphaXiv.
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