Over recent decades, sequential decision-making tasks are mostly tackled with expert systems and reinforcement learning. However, these methods are still incapable of being generalizable enough to solve new tasks at a low cost. In this article, we discuss a novel paradigm that leverages Transformer-based sequence models to tackle decision-making tasks, named large decision models. Starting from offline reinforcement learning scenarios, early attempts demonstrate that sequential modeling methods can be applied to train an effective policy given sufficient expert trajectories. When the sequence model goes large, its generalization ability over a variety of tasks and fast adaptation to new tasks has been observed, which is highly potential to enable the agent to achieve artificial general intelligence for sequential decision-making in the near future.