Token Generation

Token generation, the process of creating sequences of discrete units (tokens) representing text, images, or other data, is a core component of many large language models (LLMs) and generative AI systems. Current research focuses on improving efficiency and quality through diverse architectures like Mixture-of-Experts (MoE) models and novel decoding algorithms such as speculative decoding and early-exiting strategies, often incorporating reinforcement learning for policy optimization. These advancements aim to accelerate inference speed, enhance the quality and diversity of generated outputs, and address limitations like exposure bias and memory constraints, ultimately impacting various applications from text generation and image synthesis to question answering and financial modeling.