{"ID":2870575,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13311","arxiv_id":"2509.13311","title":"Towards General Agentic Intelligence via Environment Scaling","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.","short_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-ca...","url_abs":"https://arxiv.org/abs/2509.13311","url_pdf":"https://arxiv.org/pdf/2509.13311v1","authors":"[\"Runnan Fang\",\"Shihao Cai\",\"Baixuan Li\",\"Jialong Wu\",\"Guangyu Li\",\"Wenbiao Yin\",\"Xinyu Wang\",\"Xiaobin Wang\",\"Liangcai Su\",\"Zhen Zhang\",\"Shibin Wu\",\"Zhengwei Tao\",\"Yong Jiang\",\"Pengjun Xie\",\"Fei Huang\",\"Jingren Zhou\"]","published":"2025-09-16T17:57:20Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Language Model\"]","has_code":false}