EnvScaler Framework Automates Creation of 191 Tool-Interaction Environments for LLM Agent Training
A team of researchers has introduced EnvScaler, an automated system designed to create scalable environments for training large language model agents. This innovation tackles the challenges posed by restricted access to real-world systems, the hallucinations generated by LLM-simulated environments, and the scalability issues of manually constructed sandboxes. EnvScaler consists of two main components: SkelBuilder, which generates varied environment frameworks through topic mining, logic modeling, and quality assessment, and ScenGenerator, which develops numerous task scenarios and rule-based trajectory validation for each framework. So far, the system has produced 191 unique environments and around 7,000 scenarios, facilitating the training of Qwen3 series models via Supervised Fine-Tuning and Reinforcement Learning. This work signifies a major leap forward in automated agent training across diverse real-world simulations. The findings were published on arXiv under identifier 2601.05808v2.
Key facts
- EnvScaler is an automated framework for scalable tool-interaction environments via programmatic synthesis
- The system addresses limitations of restricted real system access, hallucination-prone LLM simulations, and hard-to-scale manual sandboxes
- SkelBuilder constructs diverse environment skeletons through topic mining, logic modeling, and quality evaluation
- ScenGenerator generates multiple task scenarios and rule-based trajectory validation functions for each environment
- The framework has synthesized 191 environments and about 7,000 scenarios
- These environments have been applied to Supervised Fine-Tuning and Reinforcement Learning for Qwen3 series models
- The research was published on arXiv with identifier 2601.05808v2
- The announcement type was replace-cross
Entities
Institutions
- arXiv