Simulate to Generalize: Scaling Stateful Supervision for API-calling Agents using LLM World Models
arXiv:2607.16900v3 Announce Type: replace Abstract: 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…