Title: One cannot stand for everyone! Leveraging Multiple User Simulators to train Task-oriented Dialogue Systems. Abstract: User simulators are agents designed to imitate human users; recent advances have found that Task-oriented Dialogue (ToD) systems optimized toward a user simulator could better satisfy the need of human users. However, this might result in a sub-optimal ToD system if it is tailored to only one \textit{ad hoc} user simulator, since human users can behave differently. 
In this paper, we propose a framework called MUST to optimize ToD systems via leveraging \textbf{m}ultiple \textbf{u}ser \textbf{s}imula\textbf{t}ors. 

The main challenges of MUST fall in 1) how to adaptively specify which user simulator to interact with the ToD system at each optimization step, since the ToD system might be over-fitted to some specific user simulators, and simultaneously under-fitted to some others; 2) how to avoid catastrophic forgetting of the adaption for a simulator that is not selected for several consecutive optimization steps.
To tackle these challenges, we formulate MUST as a Multi-armed bandits (MAB) problem and provide a method called MUST$_{\mathrm{adaptive}}$ that balances 
\textit{i}) the \textit{boosting adaption} for adaptive interactions between different user simulators and the ToD system and
\textit{ii}) the \textit{uniform adaption} to avoid the catastrophic forgetting issue.
With both automatic evaluations and human evaluations, our extensive experimental results on the restaurant search task from MultiWOZ show that the dialogue system trained by our proposed MUST achieves a better performance than those trained by any single user simulator. It also has a better generalization ability when testing with unseen user simulators. Moreover, our method MUST$_{\mathrm{adaptive}}$ is indeed more efficient and effective to leverage multiple user simulators by our visualization analysis.