We discuss the evolving role of Reinforcement Learning (RL) in Large Language Models (LLMs). Initially, RL was primarily used as a distillation technique to align LLM outputs with preferences and improve performance on verifiable tasks by leveraging LLMs' ability to verify outputs better than generate them. However, the rise of LLM-based agents marks a shift where RL enables agents to learn autonomous behaviors for complex tasks in dynamic environments, moving from refining static output to learning multi-step actions and planning. This transition involves using environmental feedback and task-based rewards to optimize agent performance, representing a significant expansion of RL's application beyond simple distillation.