The field of language processing is moving towards the development of hybrid architectures that combine the strengths of different models, such as discrete diffusion models and autoregressive models, to achieve better performance and efficiency. Diffusion models, in particular, have shown great potential in language modeling, offering advantages such as parallel generation and built-in self-correction mechanisms. Recent studies have explored the use of soft-masking, loopholing, and other techniques to improve the performance of diffusion models. Noteworthy papers include: Planner and Executor, which presents a study on hybrid architectures that couple discrete diffusion language models with autoregressive models, achieving significant accuracy gains and computational savings. Soft-Masked Diffusion Language Models introduces a novel method that dynamically blends the embedding of the mask token with the embeddings of the top-k predicted tokens, improving perplexity and MAUVE scores. Loopholing Discrete Diffusion presents a mechanism that preserves rich distributional information via a deterministic latent pathway, leading to substantial gains in generative perplexity and coherence.