Reinforcement learning (RL), where AI agents learn by trial and error, has achieved remarkable feats, from mastering Go to controlling robots. However, traditional RL often struggles with complex tasks due to inefficient state representations—the way the agent perceives its environment. Imagine trying to solve a maze while only seeing blurry shapes; you'd need many attempts to learn the layout! Similarly, RL agents often lack crucial task-specific details in their state representations, making learning slow and inefficient. This is where the power of Large Language Models (LLMs) comes in. Researchers have developed a novel approach called LLM-Empowered State Representation (LESR) that uses LLMs to generate more informative and task-relevant codes for state representations. Essentially, the LLM acts like a helpful guide, providing the RL agent with a clearer picture of its surroundings. This enhanced view enables the agent to learn much faster and achieve better performance. Think of it as giving the agent a detailed map of the maze instead of just blurry images. In experiments, LESR significantly improved sample efficiency and overall performance on various benchmark tasks, including robot manipulation and navigation. For example, in challenging maze navigation tasks where standard RL methods failed, LESR enabled the agents to successfully reach their goals. This breakthrough suggests that LLMs can play a pivotal role in boosting the capabilities of RL agents. However, there are limitations. Currently, LESR relies solely on the provided state features and doesn't incorporate external information, which can be a constraint in partially observable environments. Also, the quality of the generated representations depends on the LLM's abilities. Future work will explore integrating external information and improving the overall framework to make LESR even more powerful. As LLMs continue to advance, their ability to empower RL agents could unlock significant progress in AI, leading to more efficient and capable agents that can tackle increasingly complex real-world tasks.