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

Argues that scaling spatial world models is bottlenecked by weak reward signals (CLIP-score proxies) and proposes game development itself as the missing reward environment: a game engine acts as an executable world specification verifying collision, physics, and navigability, while human acceptance of scenes supplies the global signal. Introduces RLHEV (Reinforcement Learning with Human-Engine Verification), a post-training paradigm combining dense engine-based rewards with implicit human acceptance feedback and long-horizon trajectories from the development process.