Title: Towards General Agentic Intelligence via Environment Scaling
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: tool use, general agent

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 inter-
actions with these environments. To address these, we design a scalable frame-
work 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, τ-bench,
τ2-Bench, and ACEBench, demonstrate that our trained model, AgentScaler,
significantly enhances the models’ function-calling capability.