The field of natural language processing is witnessing a significant shift towards parallel decoding and diffusion techniques to accelerate language models. Recent research has focused on developing innovative methods to improve inference efficiency, including parallel text generation, speculative decoding, and diffusion language models. These advancements have led to significant improvements in inference speed, with some models achieving speedups of up to 50x compared to traditional autoregressive models. Notably, papers such as Temporal Self-Rewarding Language Models and Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion Forcing have introduced novel architectures and techniques that exploit temporal dynamics and parallelism to enhance model performance. Additionally, surveys like A Survey on Parallel Text Generation and A Survey on Diffusion Language Models have provided comprehensive overviews of the current landscape, highlighting the potential of these techniques for various natural language processing tasks. Overall, the field is moving towards more efficient and scalable language models, enabling faster and more accurate processing of large amounts of text data.