Masked Diffusion Language Models (MDLM) have recently demonstrated that discrete diffusion can achieve competitive performance in text generation. However, training these models remains computationally expensive, particularly for lower-resourced languages like Portuguese. In this work, we adapt REPresentation Alignment (REPA), a technique originally proposed for vision, to the textual domain. We systematically evaluate the impact of aligning the internal representations of a Portuguese MDLM with those of pretrained teacher encoders (e.g., Qwen, BERTimbau). Our experiments show that REPA significantly accelerates training and improves final perplexity by 28.6% compared to a baseline without alignment. We also identify optimal hyperparameters, finding that mid-level alignment with modern teacher encoders yields the best results.