Large language models (LLMs) are revolutionizing how we interact with technology, but their impressive capabilities come at a cost: speed. Generating text, especially in real-time applications like chatbots, can be slow due to the sequential nature of LLMs. Each word is generated one at a time, creating a bottleneck. Imagine writing a novel one letter at a time—it would take forever! That's the challenge researchers are tackling with innovative techniques like speculative decoding. One such method, called Medusa, uses "draft models" to predict multiple words at once, which are then checked by the main LLM. This parallel processing significantly boosts speed. However, Medusa has limitations. It doesn't consider the relationships *between* the predicted words, leading to inaccuracies and wasted effort. This is where Clover comes in. Clover enhances Medusa by adding a "regressive" element. It essentially teaches the draft models to learn from previously predicted words, improving their accuracy and generating more coherent text. Think of it like predictive text on your phone, but on a much grander scale. By considering the context of previous words, Clover makes smarter predictions, leading to a significant speed boost. Tests on Baichuan models, both small and large, show Clover outperforms existing methods, generating up to 50%-76% more usable text per step than Medusa. This improvement is especially noticeable in larger models and complex tasks like math problems and creative writing. Clover is a promising step towards making LLMs faster and more efficient, paving the way for even more seamless and responsive AI experiences in the future. While challenges remain in optimizing for different hardware and scaling to even larger models, Clover's innovative approach opens exciting possibilities for the future of LLM deployment.