Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but this process relies on rich and varied tool-interaction sandboxes. However, access to real systems is often restricted; LLM-simulated environments are prone to hallucinations and inconsistencies; and manually built sandboxes are hard to scale. In this paper, we propose EnvScaler, an automated framework for scalable tool-interaction environments via programmatic synthesis. EnvScaler comprises two components. First, SkelBuilder constructs diverse environment skeletons through topic mining, logic modeling, and quality evaluation. Then, ScenGenerator generates multiple task scenarios and rule-based trajectory validation functions for each environment. With EnvScaler, we synthesize 191 environments and about 7K scenarios, and apply them to Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) for Qwen3 series models. Results on three benchmarks show that EnvScaler significantly improves LLMs' ability to solve tasks in complex environments involving multi-turn, multi-tool interactions. We release our code and data at https://github.com/RUC-NLPIR/EnvScaler.

翻译：大语言模型（LLMs）被期望训练为能在各类现实环境中作为智能体执行任务，但该过程依赖于丰富多样的工具交互沙箱环境。然而，现实系统的访问往往受限；LLM模拟的环境易产生幻觉与不一致性；而人工构建的沙箱则难以扩展。本文提出EnvScaler，一种通过程序合成实现可扩展工具交互环境的自动化框架。EnvScaler包含两个核心组件：首先，SkelBuilder通过主题挖掘、逻辑建模与质量评估构建多样化的环境骨架；随后，ScenGenerator为每个环境生成多任务场景及基于规则的轨迹验证函数。利用EnvScaler，我们合成了191个环境及约7K个场景，并将其应用于Qwen3系列模型的监督微调（SFT）与强化学习（RL）训练。在三个基准测试上的结果表明，EnvScaler显著提升了LLMs在涉及多轮次、多工具交互的复杂环境中解决任务的能力。我们在https://github.com/RUC-NLPIR/EnvScaler 发布了代码与数据。