AI学会玩4人游戏，一局能跑几小时不崩
你玩过《火箭联盟》吗？车和球撞来撞去，物理反应极其复杂。现在有个AI模型，学会了同时控制4个玩家，而且一局能稳定运行几小时不崩——这比之前所有单玩家世界模型都强。它用500亿参数，在1万小时游戏录像上训练，每秒能生成20帧画面。关键创新是：它把每个玩家的操作分开处理，而不是把其他玩家当背景。这样无论玩家怎么乱动，模型都能保持场景连贯。虽然训练时只看过短片段，但实际跑起来，5分钟内的画面质量不下降，甚至能持续数小时。这不是你明天能用的工具，但它证明了AI可以理解多人互动的物理世界——未来可能用于游戏NPC、自动驾驶多车协同模拟。
📄 原文摘要(英文)
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.