Towards General Agentic Intelligence via Environment Scaling

ACL ARR 2026 January Submission9430 Authors

Keywords: agent, environment, synthetic data

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, $\tau$-bench, $\tau^2$-Bench, and ACEBench, demonstrate that our trained model, AgentScaler, significantly enhances the models’ function-calling capability.

Paper Type: Long

Research Area: AI/LLM Agents

Research Area Keywords: AI/LLM Agents, Language Modeling

Contribution Types: NLP engineering experiment

Languages Studied: English, Chinese

Submission Number: 9430