@inproceedings{fang-etal-2026-towards,
title = "Towards General Agentic Intelligence via Environment Scaling",
author = "Fang, Runnan and
Cai, Shihao and
Li, Baixuan and
Wu, Jialong and
Li, Guangyu and
Yin, Wenbiao and
Wang, Xinyu and
Wang, Xiaobin and
Su, Liangcai and
Zhang, Zhen and
Wu, Shibin and
Tao, Zhengwei and
Jiang, Yong and
Xie, Pengjun and
Zhang, Ningyu and
Huang, Fei and
Zhang, Wentao and
Zhou, Jingren",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.872/",
pages = "17610--17621",
ISBN = "979-8-89176-395-1",
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, 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, -bench, -Bench, and ACEBench, demonstrate that our trained model, AgentScaler, significantly enhances the models' function-calling capability."
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<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, 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, -bench, -Bench, and ACEBench, demonstrate that our trained model, AgentScaler, significantly enhances the models’ function-calling capability.</abstract>
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%0 Conference Proceedings
%T Towards General Agentic Intelligence via Environment Scaling
%A Fang, Runnan
%A Cai, Shihao
%A Li, Baixuan
%A Wu, Jialong
%A Li, Guangyu
%A Yin, Wenbiao
%A Wang, Xinyu
%A Wang, Xiaobin
%A Su, Liangcai
%A Zhang, Zhen
%A Wu, Shibin
%A Tao, Zhengwei
%A Jiang, Yong
%A Xie, Pengjun
%A Zhang, Ningyu
%A Huang, Fei
%A Zhang, Wentao
%A Zhou, Jingren
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F fang-etal-2026-towards
%X 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, 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, -bench, -Bench, and ACEBench, demonstrate that our trained model, AgentScaler, significantly enhances the models’ function-calling capability.
%U https://aclanthology.org/2026.findings-acl.872/
%P 17610-17621
Markdown (Informal)
[Towards General Agentic Intelligence via Environment Scaling](https://aclanthology.org/2026.findings-acl.872/) (Fang et al., Findings 2026)
ACL
- 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, Ningyu Zhang, Fei Huang, Wentao Zhang, and Jingren Zhou. 2026. Towards General Agentic Intelligence via Environment Scaling. In Findings of the Association for Computational Linguistics: ACL 2026, pages 17610–17621, San Diego, California, United States. Association for Computational Linguistics.