Can Generative Recommenders serve as User Data Simulators?

User behavior traces are the foundation of recommender systems, but high-quality traces are difficult to obtain. Online A/B testing provides direct feedback but is costly and risky, while existing logs are fixed records of past exposure policies. User simulation offers an offline alternative, yet its usefulness depends on whether generated traces provide useful signals for downstream tasks. This paper explores whether generative recommenders can serve as user data simulators. A generative recommender models the conditional distribution of future user behavior given user history, profile, and context; recommendation and user behavior simulation are two interfaces to the same learned behavior distribution. Building on this observation, we present GenSim, a unified generative user data simulation framework that turns the generative recommender backbone into a controlled simulation engine. To make the framework practical, we enhance it with two capabilities: the first on long-horizon generation, which combines test-time scaling with reinforcement learning on verifiable rewards to control repetition, context drift, and suffix degradation across generated traces; the second on new user synthesis, which synthesizes new user profiles together with compatible behavior traces to construct additional user contexts. We instantiate the framework with OpenOneRec and evaluate it along two axes: behavioral characteristics, measuring response-prediction performance and distributional alignment between generated and observed data, and downstream utility, measuring whether generated traces improve recommendation training. Our results show that generative recommenders can serve as practical engines for synthetic behavior trace generation, providing useful supervision for downstream training, with benefits varying across models and tasks.