Researchers propose Turing-RL, a reinforcement learning approach for training user simulator models based on the Turing Test. This method trains large language models to simulate human users by maximizing their ability to fool a human evaluator into thinking they are real. The approach aims to improve simulator realism and usefulness across applications like agent training and personalization evaluation.