GameWAM: A World Action Model for Video Games

arXiv:2608.26200 · cs.AI, cs.CV, cs.LG · Submitted 2026-08-25 · Read on arXiv

cs.AI, cs.CV, cs.LG

Submitted: 2026-08-25

Updated: 2026-09-10

Project page: https://yunncheng.github.io/GameWAM

Terminology

• Alignment — 14× in this paper · explained in 1 episode
• KV cache — 14× in this paper · explained in 3 episodes
• Transformer — 14× in this paper · explained in 2 episodes
• World Model — 12× in this paper · explained in 7 episodes
• Action Space — 7× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Closed-Loop Control — 6× in this paper · explained in 2 episodes
• Flow Matching — 5× in this paper · explained in 7 episodes
• Decoupling — 4× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Low-Frequency Action Source Imprinting — 3× in this paper
• Abstraction — 2× in this paper · explained in 1 episode
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Temporal consistency — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Action Masking — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Agent Architectures — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Minecraft Computer Use — 1× in this paper
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Procedural Knowledge — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Stochastic Dynamics — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 3 episodes

Sources

• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• MineStudio: A Streamlined Package for Minecraft AI Agent Development
• MineWorld: a Real-Time and Open-Source Interactive World Model on Minecraft
• MineRL: A Large-Scale Dataset of Minecraft Demonstrations
• Dream to Control: Learning Behaviors by Latent Imagination
• Mastering Atari with Discrete World Models
• Scaling Instructable Agents Across Many Simulated Worlds
• Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds
• Phenaki: Variable Length Video Generation From Open Domain Textual Description
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• When to Trust Imagination: Adaptive Action Execution for World Action Models
• Game-TARS: Pretrained Foundation Models for Scalable Generalist Multimodal Game Agents
• World Action Models are Zero-shot Policies
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?
• Matrix-Game: Interactive World Foundation Model

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