Token Optimization

Token optimization focuses on reducing the number of tokens processed in large language models (LLMs) and vision transformers (ViTs) to improve efficiency and reduce computational costs. Current research explores various techniques, including adaptive token selection, joint optimization of tokens and model architecture (e.g., channel pruning), and the use of reinforcement learning algorithms like Proximal Policy Optimization (PPO) to learn optimal token-level strategies. These advancements are significant because they address limitations in current LLMs and ViTs, such as context window size and computational expense, leading to more efficient and cost-effective models for various applications.