Large language models (LLMs) have shown remarkable progress, but their reasoning abilities often falter. Imagine an AI tackling a complex math problem, laying out its step-by-step logic like a student on a whiteboard. Sometimes, the logic makes perfect sense, but a small misstep early on throws the entire solution off track. This is where the concept of 'critique models' comes in. Researchers are exploring how specialized AI models can act as critics, providing targeted feedback to improve an LLM's reasoning process. These 'critique models' analyze the LLM's chain-of-thought, pointing out errors in logic and suggesting alternative approaches, much like a teacher guiding a student. The research team developed a method called AutoMathCritique, an automated system for training these AI critics. They generate a dataset of flawed reasoning paths, have the critic model provide feedback, and then filter out low-quality critiques. They found that these critics dramatically improve the performance of LLMs, especially on harder problems where a single misstep can derail the entire process. Importantly, the more computational power they give the LLM and its critic, the better the results. This suggests a new path toward developing even more sophisticated reasoning abilities in LLMs. By integrating these AI critics into the training process itself, the research aims to further enhance LLM’s capacity to self-correct and learn from mistakes, ultimately boosting their reasoning capabilities and problem-solving prowess. These models aren’t just for math problems. The idea could potentially be extended to many fields, from scientific research to coding and even general problem-solving. The challenge ahead lies in refining these models to ensure they provide high-quality feedback that truly helps LLMs avoid mistakes and develop robust problem-solving strategies.