Recent trends in learning wireless policies attempt to develop deep neural networks (DNNs) for handling multiple tasks with a single model. Existing approaches often rely on large models, which are hard to pre-train and fine-tune at the wireless edge. In this work, we challenge this paradigm by leveraging the structured knowledge of wireless problems -- specifically, permutation equivariant (PE) properties. We design three types of PE-aware modules, two of which are Transformer-style sub-layers. These modules can serve as building blocks to assemble compact DNNs applicable to the wireless policies with various PE properties. To guide the design, we analyze the hypothesis space associated with each PE property, and show that the PE-structured module assembly can boost the learning efficiency. Inspired by the reusability of the modules, we propose PE-MoFormer, a compositional DNN capable of learning a wide range of wireless policies -- including but not limited to precoding, coordinated beamforming, power allocation, and channel estimation -- with strong generalizability, low sample and space complexity. Simulations demonstrate that the proposed modular PE-based framework outperforms relevant large model in both learning efficiency and inference time, offering a new direction for structured cross-task learning for wireless communications.