EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL
Authors: name: Minrui Xu, name: Zilin Wang, name: Mengyi DENG, name: Zhiwei Li, name: Zhicheng Yang, name: Xiao Zhu, name: Yinhong Liu, name: Boyu Zhu, name: Baiyu Huang, name: Chao Chen, name: Heyuan Deng, name: Fei Mi, name: Lifeng Shang, name: Xingshan Zeng, name: Zhijiang Guo
Organization: LARK Lab@HKUST (GZ)

Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robust execution environments and the scarcity of realistic training data that captures implicit human reasoning. Existing approaches depend on costly real-world APIs, hallucination-prone LLM simulators, or synthetic environments that are often single-turn or depend on pre-collected documents. Moreover, synthetic trajectories are frequently over-specified, resembling instruction sequences rather than natural human intents, reducing their effectiveness for RL training. We introduce EnvFactory, a fully automated framework that addresses both challenges. EnvFactory autonomously explores and verifies stateful, executable tool environments from authentic resources, and synthesizes natural multi-turn trajectories through topology-aware sampling and calibrated refinement, producing grounded queries with implicit intents. Using only 85 verified environments across 7 domains, EnvFactory generates 2,575 SFT and RL trajectories. Despite using significantly fewer environments than prior work, which are often 5 times more, EnvFactory achieves superior training efficiency and downstream performance, improving Qwen3-series models by up to +15% on BFCLv3, +8.6% on MCP-Atlas, and +6% on conversational benchmarks including τ^2-Bench and VitaBench. By fully automating both environment construction and trajectory synthesis, EnvFactory provides a scalable, extensible, and robust foundation for Agentic RL.