General-purpose world models promise scalable policy evaluation, optimization, and planning, yet achieving the required level of robustness remains challenging. Unlike policy learning, which primarily focuses on optimal actions, a world model must be reliable over a much broader range of suboptimal actions, which are often insufficiently covered by action-labeled interaction data. To address this challenge, we propose World Action Verifier (WAV), a framework that enables world models to identify their own prediction errors and self-improve. The key idea is to decompose action-conditioned state prediction into two factors -- state plausibility and action reachability -- and verify each separately. We show that these verification problems can be substantially easier than predicting future states due to two underlying asymmetries: the broader availability of action-free data and the lower dimensionality of action-relevant features. Leveraging these asymmetries, we augment a world model with (i) a diverse subgoal generator obtained from video corpora and (ii) a sparse inverse model that infers actions from a subset of state features. By enforcing cycle consistency among generated subgoals, inferred actions, and forward rollouts, WAV provides an effective verification mechanism in under-explored regimes, where existing methods typically fail. Across nine tasks spanning MiniGrid, RoboMimic, and ManiSkill, our method achieves 2x higher sample efficiency while improving downstream policy performance by 18%.

翻译：通用世界模型有望实现可扩展的策略评估、优化与规划，但达到所需的鲁棒性仍具挑战。与主要关注最优动作的策略学习不同，世界模型必须在更广泛的次优动作范围内保持可靠性，而动作标注交互数据通常难以充分覆盖这些次优动作。为解决这一难题，我们提出世界动作验证器（WAV）框架，使世界模型能够识别自身预测错误并进行自我改进。核心思路是将动作条件状态预测分解为两个因子——状态合理性与动作可达性——并分别验证。研究表明，由于两种潜在非对称性（无动作数据的更广泛可用性及动作相关特征的低维度），这些验证问题可能比预测未来状态容易得多。利用这些非对称性，我们通过（i）从视频语料库获取的多样化子目标生成器，以及（ii）从状态特征子集推断动作的稀疏逆模型，对世界模型进行增强。通过强制实施生成子目标、推断动作与前向轨迹间的循环一致性，WAV在现有方法通常失效的欠探索区域提供了有效的验证机制。在涵盖MiniGrid、RoboMimic和ManiSkill的九个任务中，我们的方法实现了2倍的样本效率提升，同时下游策略性能提高18%。