Year: 2026
Type: conference-paper
Abstract: Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but this process relies on rich and varied tool-interaction sandboxes.However, access to real systems is often restricted; LLM-simulated environments are prone to hallucinations and inconsistencies; and manually built sandboxes are hard to scale.In this paper, we propose EnvScaler, an automated framework for scalable tool-interaction environments via programmatic synthesis.EnvScaler comprises two components.First, SkelBuilder constructs diverse environment skeletons through topic mining, logic modeling, and quality evaluation.Then, ScenGenerator generates multiple task scenarios and rule-based trajectory validation functions for each environment.With En-vScaler, we synthesize 191 environments and about 7K scenarios, and apply them to Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) for Qwen3 series models.Results on three benchmarks show that EnvScaler significantly improves LLMs' ability to solve tasks in complex environments involving multiturn, multi-tool interactions.
Institution: Renmin University of China
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G228125052
OpenAlex ID: W7166849181