💡 The paper introduces a multiplayer world model that can simulate complex physics-based environments with multiple agents. The model is trained on a large dataset of gameplay from the game Rocket League, which involves fast and tightly coupled dynamics between players. The key contribution of the paper is that the model conditions on the action streams of multiple agents, allowing it to attribute changes in the scene to the correct player and stay coherent under different combinations of actions.

The model uses a latent diffusion approach with a generative objective and is trained on short clips of gameplay. Despite this, the model is able to generate stable long-horizon rollouts, with distributional quality holding steady for up to five minutes and in practice continuing for hours without collapsing. The model is also able to produce four-player matches in real time, generating 20 frames per second on a single GPU.

The paper systematically investigates the design choices behind the model, including the video codec, generative objective, and multiplayer conditioning scheme. It also characterizes how the model's behavior changes with scale, including the capabilities that emerge and the failure modes that persist. The authors develop targeted evaluations that probe the model's physical understanding, rather than just its visual appearance.

The paper's contributions include the introduction of the first multiplayer world model for highly dynamic environments, the development of a latent diffusion model that can generate stable long-horizon rollouts, and the creation of a large-scale dataset and codebase for continued research on multiplayer world models. The authors release their dataset, codebase, and a live demo to support further research in this area. Overall, the paper presents a significant advance in the development of world models that can simulate complex multiplayer environments.