The application of artificial intelligence technology has extended from relatively static tasks such as classification, translation, and question answering to relatively dynamic tasks that require a series of “interaction-action” with the environment to be completed, like autonomous driving, robotic control, and games. The core of the model for executing such tasks is the sequential decision-making (SDM) algorithm. As it faces higher uncertainties of the environment and interaction and these tasks are often safety-critical systems, the testing techniques are confronted with great challenges. The existing testing technologies for intelligent algorithm models mainly focus on the reliability of a single model, the generation of diverse test scenarios for complex tasks, simulation testing, etc., while no attention is paid to the “interaction-action” decision sequence of the SDM model, leading to unadaptability or low cost-effectiveness. In this study, a fuzz testing method named IIFuzzing for intervening in the execution of inert “interaction-action” decision sequences is proposed. In the fuzz testing framework, by learning the “interaction-action” decision sequence pattern, the inert “interaction-action” decision sequences that will not trigger failure accidents are predicted and the testing execution of such sequences is terminated to improve the testing efficiency. The experimental evaluations are conducted in four common test configurations, and the results show that compared with the latest fuzz testing for SDM models, IIFuzzing can detect 16.7%–54.5% more failure accidents within the same time, and the diversity of accidents is also better than that of the baseline approach.