TapOut is a newly proposed method that introduces a bandit-based approach to dynamic speculative decoding for large language models (LLMs), aiming to enhance their efficiency in text generation. By intelligently determining the optimal number of tokens to draft during decoding, TapOut seeks to maximize processing speed without compromising accuracy. This approach addresses a key challenge in the deployment of LLMs, where balancing speed and output quality is critical. The method has been detailed in a recent arXiv publication, highlighting its potential to improve LLM performance. While the claims of TapOut’s effectiveness are positive, they remain in the proposed or unverified stage, indicating that further validation is needed. TapOut’s innovation lies in its dynamic adjustment mechanism, which contrasts with static decoding strategies. Overall, this development represents a promising direction in the ongoing effort to optimize large-scale language model inference.