Large language models (LLMs) are impressive, but they still make mistakes, especially when asked to generate text under specific constraints. Imagine asking an LLM to write a six-word sentence—it might give you five, seven, or even a whole paragraph! But what if the LLM could learn from its mistakes? New research explores how to teach LLMs to self-correct using a clever technique called 'corrective feedback.' Researchers developed a system called CORGI (Controlled Generation with RL for Guided Interaction) that simulates a conversation between an LLM and a 'critic.' The critic provides feedback on the LLM's attempts to follow instructions, giving it a score and suggesting improvements. The LLM then uses this feedback to refine its output, trying again until it gets it right. This iterative process is powered by reinforcement learning (RL), where the LLM is 'rewarded' for producing better outputs. The surprising finding? LLMs trained with CORGI not only get better at the tasks they practiced but also improve on completely *new* tasks they've never seen before. This suggests LLMs aren’t just memorizing solutions—they're learning a general 'meta-skill' of incorporating feedback to improve. This breakthrough could revolutionize how we use LLMs, leading to more reliable and accurate AI assistants, educational tools, and more. Imagine an AI writing assistant that learns your style preferences as you give it feedback, or a chatbot that can handle complex requests by clarifying ambiguities through conversation. While the research focuses on tasks with clear 'right' and 'wrong' answers, it opens exciting possibilities for teaching AI to self-improve in more nuanced, creative domains.