arxiv:2604.18292

Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence

Published on Apr 20

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KABI on Apr 21

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Authors:

Guanting Dong ,

Shijue Huang ,

Abstract

Agent-World introduces a self-evolving training framework that advances general agent intelligence through autonomous environment discovery and continuous learning across diverse real-world scenarios.

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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 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.

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dongguanting

Paper author Paper submitter about 19 hours ago

We introduce Agent-World , a general-purpose agent training arena that couples real-world environment synthesis with continuous self-evolving training, forming a closed loop in which agents and environments co-evolve.

It consists of two parts:

(1) Agentic environment–task discovery . A deep-search agent, anchored on real-world environment themes, autonomously mines environment databases from the web, generates executable tools, and synthesizes verifiable tasks.

(2) Continuous self-evolving training . Agents are trained with multi-environment reinforcement learning, while the synthesized environments serve as a training arena that automatically diagnoses capability gaps and targets environment/task expansion, enabling sustained self-evolution.

In total, Agent-World builds 1,978 environments and 19,822 tools, with synthesized tasks averaging more than 15 interaction turns .

Across 23 challenging benchmarks (including $\tau^2$-Bench, BFCL V4, MCP-Mark, ClawEval, SkillsBench, etc.), Agent-World-8B/14B consistently outperforms existing environment-scaling methods and strong open-source foundation models. Further analyses reveal a clear scaling relationship among environment diversity, self-evolution rounds, and agent performance.