邁向通用智能代理：通過環境擴展

摘要

高階的代理智能是將大型語言模型部署於實際應用中的先決條件。多樣化的現實世界API要求精確且穩健的函數調用智能，這需要代理在各種環境中通過互動來發展這些能力。函數調用能力的廣度與代理訓練環境的多樣性密切相關。在本研究中，我們通過擴展環境作為提升通用代理智能的一步，這引發了兩個核心挑戰：(i) 如何以系統化的方式擴展環境，以及(ii) 如何從與這些環境互動中獲得的經驗中有效訓練代理能力。為解決這些問題，我們設計了一個可擴展的框架，該框架自動構建完全模擬的異質環境，從而系統性地拓寬函數調用場景的空間。我們進一步採用了一種兩階段的代理微調策略：首先賦予代理基礎的代理能力，然後針對特定領域進行專業化。在代理基準測試、tau-bench、tau2-Bench和ACEBench上的廣泛實驗表明，我們訓練的模型AgentScaler顯著增強了模型的函數調用能力。

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.

邁向通用智能代理：通過環境擴展

Towards General Agentic Intelligence via Environment Scaling

摘要

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