Autonomous driving requires reasoning about how ego actions shape future world evolution, rather than merely mapping observations to actions. However, most end-to-end methods rely on direct state-to-action imitation, while existing world models often remain weakly aligned with downstream policy generation. We introduce Discrete-WAM, a unified discrete vision-action world-policy framework that represents visual observations, future states, high-level decisions, and ego actions within a shared token space. Built on this discrete alignment, Discrete-WAM jointly trains world modeling, world-policy modeling, and policy modeling through multi-task and multi-stage pretraining, allowing action-conditioned future prediction to directly support policy generation. For downstream planning, Discrete-WAM further decomposes policy generation into hierarchical decision prediction and parallel action-token editing, where the decision token provides a high-level planning skeleton and confidence-based scheduling refines dense future actions efficiently. Experiments on large-scale autonomous-driving benchmarks show that Discrete-WAM achieves strong planning performance while supporting controllable future generation, counterfactual evaluation, surprise-based world-model analysis, and efficient parallel policy decoding. These results suggest that discrete representation alignment, unified world-policy training, and hierarchical token editing provide a promising design paradigm for physical AI.

翻译：自主驾驶需要推理自我动作如何塑造未来世界演化，而不仅仅是建立观测到动作的映射。然而，大多数端到端方法依赖直接的状态到动作模仿，而现有世界模型往往与下游策略生成的关联较弱。我们提出Discrete-WAM，一个统一的离散视觉-动作世界-策略框架，在共享令牌空间中表示视觉观测、未来状态、高层决策和自我动作。基于该离散对齐，Discrete-WAM通过多任务和多阶段预训练联合训练世界建模、世界-策略建模和策略建模，使得动作条件下的未来预测能够直接支持策略生成。对于下游规划，Discrete-WAM进一步将策略生成分解为分层决策预测与并行动作令牌编辑，其中决策令牌提供高层规划骨架，而基于置信度的调度则高效地细化密集未来动作。在大规模自主驾驶基准上的实验表明，Discrete-WAM在实现强规划性能的同时，支持可控未来生成、反事实评估、基于惊奇的世界模型分析以及高效并行策略解码。这些结果表明，离散表征对齐、统一世界-策略训练以及分层令牌编辑为物理AI提供了一种有前景的设计范式。