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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-executab
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