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

Source: https://huggingface.co/papers/2604.18292 Published on Apr 20

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Submitted byhttps://huggingface.co/dongguanting

KABIon Apr 21

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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.

Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. TheModel Context Protocol(MCP) and broaderagent skillsoffer 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, aself-evolving training arenafor 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 combinesmulti-environment reinforcement learningwith a self-evolving agent arena that automatically identifies capability gaps throughdynamic task synthesisand drives targeted learning, enabling theco-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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