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Agentic Game Development as a Verifiable Trajectory Data Engine for Scaling World Models

185 upvotes · 26 AUG 2026 · Pengfei Zhou, Hexin Wang, Zhengfeiyang Zhang et al.

This paper proposes a way to improve the efficiency of training world models by using game development as a source of reward signals and trajectory data, allowing for more effective post-training of large language models using reinforcement learning. Practitioners might care about this approach because it could lead to more scalable and effective world models for applications like dialogue systems and visual question answering.

game developmentworld modelsreinforcement learningtrajectory dataverification signals