做Agentic RL的团队终于有了自动化环境构建方案——EnvFactory只用85个环境就碾压了此前5倍数据量的方法，想省掉手动造环境成本的开发者可以直接用。
EnvFactory是一个全自动框架，解决了Agentic RL中可扩展执行环境缺失和真实训练数据稀缺两大瓶颈。它从真实资源中自主探索并验证有状态、可执行的工具环境，通过拓扑感知采样和校准精炼合成自然的多轮轨迹，生成带有隐式意图的查询。仅用85个已验证环境（远少于此前工作的5倍以上），EnvFactory就生成了2575条SFT和RL轨迹，并在BFCLv3、MCP-Atlas等基准上提升Qwen3系列模型最高15%。该框架完全自动化环境构建和轨迹合成，为Agentic RL提供了可扩展、可扩展且鲁棒的基础。
EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL
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.