We provide a statistically rigorous guidelines for training interactive, multi-step LLM agents, exploring optimal compute allocation, generalization, and hyperparameter settings.

A framework that synthesizes a tuple of optimal control policies for multi-agent systems that maximizes the probability of satisfying a desired hyperproperty.