Simulate to Generalize: Scaling Stateful Supervision for API-calling Agents using LLM World Models
Training agents that generalize to unseen, stateful environments requires a massive dataset of state-changing trajectories covering a vast and diverse set of APIs. However, scaling this broad supervision is severely bottlenecked by the immense effort required to implement and populate fully-executable environments across a broad spectrum of domains. To bypass this barrier, we introduce a data generation pipeline that decouples data synthesis from environment construction by leveraging LLMs as digital world models. Starting from only a list of broad domain names, our automated pipeline synthesizes diverse APIs and tasks. To produce trajectories, a teacher agent iteratively solves these tas...
| Authors | Seanie Lee, Sanjoy Chowdhury, Chao Jiang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 July 2026 |
| Last updated at source | 22 September 2026 |
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