🎮 Code agents thrive because code is executable and verifiable. This paper brings the same idea to game development to accelerate world model training. Title: Agentic Game Development as a Verifiable Trajectory Data Engine for Scaling World Models URL: 🧩 Overview The paper identifies a "verifiability bottleneck": fuzzy proxies like CLIP scores are noisy and gameable as reward signals. It proposes tapping game engines' collision, physics, and navmesh checks as a dense, trustworthy reward source instead. ❗ Problem it solves Spatial generation models (video, 3D, world models) lack reliable reward signals, so scaling compute doesn't reliably translate into real capability — what the authors call the "unverifiability tax." 🛠 Method RLHEV combines dense engine-verified rewards with sparse human acceptance judgments. AWoMo runs a Propose-Render-Verify-Repair-Review loop as a developer agent. UWDP unifies development traces into a single structured protocol for training data. 📊 Results On UnitySceneBench, full RLHEV beats the fuzzy-proxy baseline by +0.098. Cross-engine generalization (Unity to Godot) rises from 0.15 to 0.35. Downstream, it boosts D4RL Gym-MuJoCo by +48.43% and R2R navigation by +0.79%. #WorldModels# #ReinforcementLearning#