Reinforcement learning (RL) has made impressive strides, but it's notoriously data-hungry. Imagine teaching a robot a new skill – it needs tons of trial and error before it gets things right. This 'low sample efficiency' has been a major roadblock. But now, Large Language Models (LLMs), like the ones powering chatbots, are offering a clever shortcut. New research demonstrates how LLMs can inject 'background knowledge' into the RL process, drastically cutting down the learning time. The key idea is to give the LLM a general overview of the environment, like a basic understanding of the laws of physics. For instance, in a simulated world, the LLM might learn that walls and obstacles are bad and food is good. This general wisdom, gathered by simply observing some initial gameplay, can be translated into 'reward signals' for the RL agent. So, instead of stumbling blindly through a million tries, the agent now has some basic instincts to guide its actions. The researchers tested three ways to extract this knowledge from the LLM, all with impressive results. In both simple grid worlds and complex crafting scenarios, the LLM-guided RL agents learned much faster. This means that instead of coding specific rules for each task, we can let LLMs provide basic guidelines for how to interact with the world. This approach has the potential to unlock more complex and adaptable AI agents, capable of learning new skills much more efficiently. While this research is a significant leap, it has limitations. The prompts used to extract knowledge still need some manual fine-tuning, and the quality of the extracted knowledge depends on the LLM's capability. Further research is needed to streamline and automate these processes and to find the best ways to extract the richest possible insights from the LLM.