Abstract
Advanced agentic intelligence is a prerequisite for deploying Large Language Models in practical, real-world applications. Diverse real-world APIs demand precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments. The breadth of function-calling competence is closely tied to the diversity of environments in which agents are trained. In this work, we scale up environments as a step towards advancing general agentic intelligence. This gives rise to two central challenges: (i) how to scale environments in a principled manner, and (ii) how to effectively train agentic capabilities from experiences derived through interactions with these environments. To address these, we design a scalable framework that automatically constructs heterogeneous environments that are fully simulated, systematically broadening the space of function-calling scenarios. We further adapt a two-phase agent fine-tuning strategy: first endowing agents with fundamental agentic capabilities, then specializing them for domain-specific contexts. Extensive experiments on agentic benchmarks, tau-bench, tau2-Bench, and ACEBench, demonstrate that our trained model, AgentScaler, significantly enhances the function-calling capability of models.
Abstract (translated)
高级代理智能是部署大型语言模型于实际、现实世界应用的先决条件。各种各样的现实世界API要求精确而稳健的功能调用智能，这需要通过在不同环境中互动来培养这些能力。功能调用能力的广度与训练代理所处环境的多样性紧密相关。在这项工作中，我们扩大了环境规模以推进通用代理智能的发展。这引发了两个核心挑战：（i）如何以合理的方式扩展环境；（ii）如何有效地从与这些环境互动的经验中培养代理的能力。为解决这些问题，我们设计了一个可扩展框架，该框架可以自动构建异构的、完全模拟的环境，并系统地拓宽功能调用场景的空间范围。此外，我们还适应了一种两阶段的代理微调策略：首先赋予代理基本的代理能力，然后针对特定领域情境对其进行专门化处理。在agentic基准（如tau-bench, tau2-Bench 和 ACEBench）上的广泛实验表明，我们的训练模型AgentScaler显著提升了模型的功能调用能力。 这段翻译介绍了如何通过扩大模拟环境规模来提升大型语言模型的代理智能，并详细描述了一种两阶段微调策略和一个可扩展框架的设计。实验结果也证明了这种新方法的有效性。
URL
https://arxiv.org/abs/2509.13311
