Large language models (LLMs) have revolutionized how we interact with text, but their autoregressive nature—generating text one token at a time—creates a speed bottleneck. Imagine if LLMs could predict multiple words simultaneously. That's the promise of a new technique called Self-Distillation Through Time (SDTT), which drastically accelerates text generation in diffusion language models. Unlike traditional LLMs, diffusion models corrupt text into noise and then learn to reverse this process, gradually reconstructing the original text. While powerful, this denoising process can be computationally intensive. SDTT tackles this challenge by training a “student” diffusion model to mimic the behavior of a more computationally expensive “teacher” model that uses many denoising steps. The student model learns to generate high-quality text in a fraction of the steps, achieving comparable or even better results. This innovative approach reduces the number of denoising steps by a factor of 32 to 64, leading to up to an 8x speed increase in text generation compared to traditional LLMs, even those employing key-value caching. Tests on the LAMBADA dataset, which measures language understanding, showed that SDTT doesn't sacrifice accuracy for speed. In fact, models using SDTT displayed comparable or even improved performance. This speed boost has major implications for applications that rely on rapid text generation, like real-time chatbots, translation, and content creation. SDTT also unlocks the potential for more extensive exploration of solutions in complex reasoning tasks, which often rely on generating and evaluating many different text completions. The development of SDTT is an exciting step towards making LLMs faster and more efficient, while opening doors to tackling even more challenging AI problems. Further research will focus on applying SDTT to larger models and exploring its potential in computationally intensive tasks like theorem proving and complex reasoning.