Imagine a future where self-driving cars navigate busy roads not just individually, but as a team, anticipating each other's moves and seamlessly merging into traffic. Researchers are exploring this very possibility using multi-agent reinforcement learning (MARL), a technique where AI agents learn through trial and error within a shared environment. However, training these AI drivers is complex and computationally expensive. A recent research paper proposes a novel solution: using large language models (LLMs), like those powering chatbots, to guide the learning process. This approach involves a 'teacher-student' model, where the LLM acts as the expert instructor, providing initial guidance and demonstrations to the smaller, MARL-based 'student' agents. These student agents then refine their driving skills through practice, eventually surpassing the teacher’s performance in simulations. Specifically, the LLM-teacher analyzes the driving scenario, predicts the intentions of other vehicles, and recommends actions to the student agents. A key innovation is the use of 'agent tools' within the LLM, which help it assess risk and resolve potential conflicts, such as merging collisions. These tools allow the LLM to reason about the driving environment in a more structured and effective way. Results from simulated highway merging scenarios are promising. The AI drivers trained with LLM guidance learned faster and achieved better performance than those trained with traditional MARL methods, demonstrating fewer collisions and smoother traffic flow. This research suggests that LLMs can play a crucial role in accelerating the development of cooperative driving, paving the way for safer and more efficient autonomous transportation systems. However, further research is needed to test these methods in more complex and realistic environments, addressing challenges like real-time decision-making and the computational cost of LLMs.