Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| author | Dong, Guanting Lu, Junting Huang, Junjie Zhong, Wanjun Liu, Longxiang Huang, Shijue Li, Zhenyu Zhao, Yang Song, Xiaoshuai Li, Xiaoxi Jin, Jiajie Zhu, Yutao Wang, Hanbin Lei, Fangyu Luo, Qinyu Chen, Mingyang Chen, Zehui Feng, Jiazhan Wen, Ji-Rong Dou, Zhicheng |
| author_facet | Dong, Guanting Lu, Junting Huang, Junjie Zhong, Wanjun Liu, Longxiang Huang, Shijue Li, Zhenyu Zhao, Yang Song, Xiaoshuai Li, Xiaoxi Jin, Jiajie Zhu, Yutao Wang, Hanbin Lei, Fangyu Luo, Qinyu Chen, Mingyang Chen, Zehui Feng, Jiazhan Wen, Ji-Rong Dou, Zhicheng |
| contents | Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model Context Protocol (MCP) and broader agent skills offer a unified interface for connecting agents with scalable real-world services, but training robust agents remains limited by the lack of realistic environments and principled mechanisms for life-long learning. In this paper, we present \textbf{Agent-World}, a self-evolving training arena for advancing general agent intelligence through scalable environments. Agent-World has two main components: (1) Agentic Environment-Task Discovery, which autonomously explores topic-aligned databases and executable tool ecosystems from thousands of real-world environment themes and synthesizes verifiable tasks with controllable difficulty; and (2) Continuous Self-Evolving Agent Training, which combines multi-environment reinforcement learning with a self-evolving agent arena that automatically identifies capability gaps through dynamic task synthesis and drives targeted learning, enabling the co-evolution of agent policies and environments. Across 23 challenging agent benchmarks, Agent-World-8B and 14B consistently outperforms strong proprietary models and environment scaling baselines. Further analyses reveal scaling trends in relation to environment diversity and self-evolution rounds, offering insights for building general agent intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18292 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence Dong, Guanting Lu, Junting Huang, Junjie Zhong, Wanjun Liu, Longxiang Huang, Shijue Li, Zhenyu Zhao, Yang Song, Xiaoshuai Li, Xiaoxi Jin, Jiajie Zhu, Yutao Wang, Hanbin Lei, Fangyu Luo, Qinyu Chen, Mingyang Chen, Zehui Feng, Jiazhan Wen, Ji-Rong Dou, Zhicheng Artificial Intelligence Computation and Language Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model Context Protocol (MCP) and broader agent skills offer a unified interface for connecting agents with scalable real-world services, but training robust agents remains limited by the lack of realistic environments and principled mechanisms for life-long learning. In this paper, we present \textbf{Agent-World}, a self-evolving training arena for advancing general agent intelligence through scalable environments. Agent-World has two main components: (1) Agentic Environment-Task Discovery, which autonomously explores topic-aligned databases and executable tool ecosystems from thousands of real-world environment themes and synthesizes verifiable tasks with controllable difficulty; and (2) Continuous Self-Evolving Agent Training, which combines multi-environment reinforcement learning with a self-evolving agent arena that automatically identifies capability gaps through dynamic task synthesis and drives targeted learning, enabling the co-evolution of agent policies and environments. Across 23 challenging agent benchmarks, Agent-World-8B and 14B consistently outperforms strong proprietary models and environment scaling baselines. Further analyses reveal scaling trends in relation to environment diversity and self-evolution rounds, offering insights for building general agent intelligence. |
| title | Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2604.18292 |