World2Policy: Continual Policy Improvement via Scenario Curriculum in Interactive Gaussian Worlds

Interactive Gaussian worlds enable photorealistic closed-loop simulation for autonomous driving. However, sustained policy improvement requires addressing two coupled challenges: how to resolve heterogeneous safety constraints during policy learning, and which scenarios should support continual policy update. A monolithic policy often entangles avoidance behaviors across dynamic actors, static obstacles, and road boundaries, while fixed scenario distributions yield diminishing returns as the policy improves. We present **World2Policy**, a co-evolutionary framework that unifies policy optimization with failure-conditioned scenario generation. In the *Policy Optimization Engine*, three constraint-specialized experts decouple safety modeling across foreground dynamics, static hazards, and drivable-area constraints through constrained reinforcement learning, while a privileged selector evaluates their complementary proposals to balance safety, progress, and comfort. In the *Environment Engine*, closed-loop rollouts expose residual failures, which guide the synthesis of physically plausible scenarios near the policy's capability boundary and update the training curriculum for policy optimization. Experiments on the closed-loop HUGSIM benchmark demonstrate that integrating World2Policy with UniAD, VAD, and LTF achieves HD-Score gains of +4.9, +6.1, and +6.9 points, respectively. Crucially, the co-evolutionary loop continually advances the policy's capability and sustains improvements across successive training rounds.