Towards General Agentic Intelligence via Environment Scaling
Authors: name: Runnan Fang, name: Shihao Cai, name: Baixuan Li, name: Jialong Wu, name: Guangyu Li, name: Wenbiao Yin, name: Xinyu Wang, name: Xiaobin Wang, name: Liangcai Su, name: Zhen Zhang, name: Shibin Wu, name: Zhengwei Tao, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou

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.