第一个能实时多人互动的世界模型，Rocket League里5B参数跑20fps，还开源了全部代码和数据，值得看看。
该研究提出了首个多人世界模型，适用于Rocket League等动态物理环境。模型使用5B参数潜扩散架构，在单张Nvidia B200 GPU上以20fps实时生成4人比赛。训练数据为10,000小时游戏回放，模型在5分钟评估窗口内保持分布质量稳定，实际可稳定运行数小时。研究者系统分析了视频编解码器、生成目标和多人条件方案等设计选择。
Multiplayer Interactive World Models with Representation Autoencoders
We introduce the first multiplayer world model for highly dynamic environments governed by complex physical interactions. Whereas single-player world models treat the other agents as part of the environment, ours conditions on the action streams of multiple agents, learning to attribute changes in the scene to the correct player and to stay coherent under arbitrary combinations of their actions. We study this problem in the game of Rocket League, where players compete and cooperate under fast, tightly coupled dynamics. Trained on 10,000 hours of gameplay collected with publicly available bots, our 5-billion-parameter latent diffusion model generates four-player matches in real time, producing 20 frames per second on a single Nvidia B200 GPU. Although trained only on short clips, its rollouts stay stable far beyond the training horizon: distributional quality holds steady out to five minutes, the longest horizon we measure, and in practice we observe rollouts continuing for hours with no sign of collapse. We systematically investigate the central design choices: the video codec, the generative objective, and the multiplayer conditioning scheme. In addition, we characterize how behavior changes with model and data scale, including the capabilities that emerge and the failure modes that persist. We further develop targeted evaluations that probe the model's physical understanding rather than visual appearance alone. To support continued research on multiplayer world models, we release our dataset, our full training and inference codebase, and a live demo.