Reinforcement learning (RL), a powerful technique for training AI agents, often stumbles due to the difficulty of designing effective reward systems. Imagine trying to teach a dog a trick with vague or inconsistent rewards – it wouldn't learn very quickly! Similarly, AI struggles when the feedback it receives is unclear. A common approach, preference-based reinforcement learning (PbRL), relies on human feedback to guide the AI, but this can be time-consuming and expensive. New research explores a fascinating alternative: using large language models (LLMs) to automate this process. The idea is to let LLMs analyze the AI’s actions, generate preferences, and even construct reward functions. This approach, called LLM4PG, essentially puts an LLM in the role of a virtual teacher, providing the AI with more consistent and nuanced feedback. Experiments in simulated environments showed promising results, with the AI converging on optimal solutions much faster than traditional methods. For example, in a task where an agent needs to navigate a maze to find a key, LLM4PG significantly sped up training. Similarly, an agent trying to cross lava flows learned much faster. These findings suggest LLMs could hold the key to accelerating RL across various domains. This could lead to more efficient training of robots, game AI, and even complex systems like power grids. However, there are challenges ahead, such as providing real-time feedback for dynamic tasks and exploring how to use multimodal LLMs that can analyze images and videos alongside text descriptions. The potential is vast, and future research may unlock even more powerful ways for LLMs to shape the future of AI.