Towards General Agentic Intelligence via Environment Scaling
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.
68ca16c06e0073c09bd1de1d
Runnan Fang
68ca16c06e0073c09bd1de1e
Shihao Cai
68ca16c06e0073c09bd1de1f
Baixuan Li
68ca16c06e0073c09bd1de20
Jialong Wu
claimed_verified
2025-09-17T12:56:52.444Z
644a4fbc2166258fccc664bc
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/8k3b44MbhQiWuo6i8BnYl.jpeg
Jialong Wu
user
callanwu
68ca16c06e0073c09bd1de21
Guangyu Li
68ca16c06e0073c09bd1de22
Wenbiao Yin
68ca16c06e0073c09bd1de23
Xinyu Wang
68ca16c06e0073c09bd1de24
Xiaobin Wang
68ca16c06e0073c09bd1de25
Liangcai Su
claimed_verified
2025-09-17T12:56:56.416Z
677f945ea82c316db164a180
/avatars/50ec99d971564944de3b1d9c17d50cfd.svg
Liangcai Su
user
HKU-Liangcai
68ca16c06e0073c09bd1de26
Zhen Zhang
68ca16c06e0073c09bd1de27
Shibin Wu
68ca16c06e0073c09bd1de28
Zhengwei Tao
68ca16c06e0073c09bd1de29
Yong Jiang
68ca16c06e0073c09bd1de2a
Pengjun Xie
68ca16c06e0073c09bd1de2b
Fei Huang
68ca16c06e0073c09bd1de2c
Jingren Zhou
Towards General Agentic Intelligence via Environment Scaling
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.
A scalable framework and two-phase fine-tuning strategy enhance function-calling capabilities of agents in diverse environments, improving performance on agentic benchmarks.
agentic intelligence Large Language Models function-calling intelligence heterogeneous environments two-phase agent fine-tuning tau-bench tau2-Bench ACEBench AgentScaler