Imagine teaching an AI to think like a detective, piecing together clues and filling in the missing pieces to crack a case. That's the essence of RATIONALYST, a new approach to supercharging AI's reasoning abilities. Large Language Models (LLMs), despite their impressive language skills, often struggle with complex reasoning. They sometimes make logical leaps, skipping over crucial steps in their thought processes, much like how we leave things unsaid in everyday conversations. This is where RATIONALYST steps in. Researchers are using an innovative pre-training technique to teach AI to unearth these implicit rationales, the hidden connections that drive our understanding. Think of it like reading between the lines. RATIONALYST is trained on a massive collection of rationales, extracted from diverse sources like the vast online text repository known as the Pile, as well as specialized reasoning datasets. This allows it to learn the underlying logic that connects different pieces of information. What sets RATIONALYST apart is its ability to act as a reasoning supervisor. When another LLM, like a general-purpose language model, is trying to solve a problem, RATIONALYST acts as a guide, providing helpful cues at each step. It's like having a seasoned mentor whispering advice to steer the reasoning process towards the correct conclusion. This approach offers a significant improvement in reasoning accuracy across diverse tasks, from mathematical equations to commonsense puzzles. The results demonstrate the power of this process-supervision approach, leading to more accurate and robust AI problem-solving. RATIONALYST is not just about achieving correct answers; it's about building a more transparent and understandable AI. By revealing the 'hidden thoughts' in the reasoning process, RATIONALYST makes it easier for us to understand how the AI reaches its conclusions. This transparency is especially valuable in areas like coding and advanced mathematics where the step-by-step logic is critical. While the initial research has shown promise, the RATIONALYST approach is not without its limitations. More exploration is needed to refine the model's training and scale it up with even larger datasets and more powerful AI models. Nonetheless, the concept of training AI to understand and utilize implicit rationales is a significant leap forward. It opens doors to more sophisticated AI systems that can reason more effectively and provide more understandable solutions, potentially revolutionizing the way AI tackles a wide range of complex challenges.