迈向通用智能代理：通过环境扩展实现

摘要

高级代理智能是大型语言模型在实际应用中部署的先决条件。多样化的现实世界API要求精确、稳健的函数调用智能，这需要代理在多变环境中通过交互来发展这些能力。函数调用能力的广度与代理训练环境的多样性密切相关。在本研究中，我们通过扩展环境作为提升通用代理智能的一步，由此引出两大核心挑战：(i) 如何以原则性的方式扩展环境，以及(ii) 如何从与这些环境交互获得的经验中有效训练代理能力。为此，我们设计了一个可扩展框架，自动构建完全模拟的异构环境，系统性地拓宽函数调用场景的空间。我们进一步采用两阶段代理微调策略：首先赋予代理基础代理能力，随后针对特定领域进行专业化。在代理基准测试tau-bench、tau2-Bench和ACEBench上的大量实验表明，我们训练的模型AgentScaler显著增强了模型的函数调用能力。

English

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.

迈向通用智能代理：通过环境扩展实现

Towards General Agentic Intelligence via Environment Scaling

摘要

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