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The computational design of polymers with targeted macroscopic properties is frequently limited by the tendency of generative models to produce structures that deviate from experimental reality. Standard evaluation metrics, originally derived for small drug-like molecules, often do not adequately capture the physicochemical constraints unique to polymeric materials. To address this, we introduce PoGE (Polymer Generation and Evaluation), a transformer-based framework trained on a hybrid corpus of generated and experimentally validated polymer representations. We propose a physics-driven evaluation suite that utilizes Wasserstein distance to quantify the alignment between generated and experimental property distributions, replacing traditional fragment-based scoring. PoGE demonstrates superior fidelity to the physicochemical attributes of real-world polymers-specifically regarding molecular weight, topological polar surface area, and chain flexibility-when compared to existing recurrent neural network and conditional generation baselines. Notably, this alignment is achieved through unconditional sequence modeling, indicating that the architecture implicitly captures the complex structural rules governing polymer synthesis without requiring explicit descriptor conditioning. By providing a rigorously validated pre-training corpus and a physics-informed benchmarking framework, this work establishes a reliable foundation for the on-demand inverse design of synthesizable materials for advanced applications.