This paper explains how to turn large language models (LLMs) from quiet students that only answer questions into active agents that can plan, act, and learn over time.
Machine learning agents usually improve by writing code, running it for hours, and then using the results to tweak the next try, which is very slow.
Traditional self-driving used separate boxes for seeing, thinking, and acting, but tiny mistakes in early boxes could snowball into big problems later.