Imagine teaching a robot to perform complex tasks, not through intricate coding, but using simple, everyday language. That's the promise of Code LLMs (Large Language Models), AIs trained to translate human instructions into robot actions. However, current open-source Code LLMs often stumble, producing buggy programs that could send your robot on a wild goose chase. Researchers have introduced a clever new system called Robo-Instruct to help these LLMs learn faster and more accurately. Robo-Instruct acts like a patient tutor, using a simulated robot environment (RoboSim) to test the LLM's code before it's unleashed on a real robot. This simulator dynamically creates a virtual world based on the code, checking for errors like trying to pick up an object that isn't there or giving conflicting commands. But what if the code works in the simulator but doesn't quite match what you intended? That's where InstAlign comes in. This clever component uses another LLM to refine the original instructions, ensuring they perfectly reflect the code's actions. The results are impressive. A relatively small Code LLM, when trained with Robo-Instruct, outperforms much larger models and even some proprietary solutions like GPT-3.5-Turbo and Gemini-Pro in generating robot control programs. This means we can create more efficient and private AI assistants for robots, without relying on massive, resource-intensive models. Robo-Instruct is a significant step towards a future where anyone can easily instruct robots using natural language, opening up exciting possibilities for homes, workplaces, and beyond. While the system currently relies on a simpler error-correction method, future research aims to integrate more sophisticated techniques, further enhancing the quality and reliability of robot programming through language.