Multiplayer Interactive World Models with Representation Autoencoders
This paper introduces the first world model that conditions on simultaneous action streams from multiple agents, learning to correctly attribute scene changes to individual players rather than treating co-players as background. Trained on 10,000 hours of Rocket League gameplay, the 5B-parameter latent diffusion model generates four-player matches in real time at 20 fps on a single Nvidia B200 GPU. Despite training on short clips, rollouts remain stable for over five minutes in testing.
Why it matters
236 HF Daily upvotes (July 7); extends world models from single-agent to genuinely multiplayer settings—a prerequisite for using world models to evaluate and train multi-agent policies. Dataset, training code, and inference codebase are released publicly.
Importance: 4/5
236 HF Daily upvotes (+1 for ≥100); first multiplayer interactive world model; full open-source release.