Large Language Models (LLMs) have made strides in various fields, but math remains a challenge. Their step-by-step reasoning, while promising, is often prone to errors that snowball into incorrect answers. Existing methods try to address this by using 'verifiers' to check the LLM's work, but these verifiers often lack the nuance to catch subtle mistakes within each step. A new research paper proposes a smarter verifier called the Token-Supervised Value Model (TVM). Imagine a teacher not just marking a math problem wrong, but pointing out the exact moment the student made a mistake. TVM does something similar. It analyzes each tiny piece of the LLM's reasoning (each 'token') and predicts how likely that piece is to lead to the right answer. This 'token-level supervision' allows TVM to give more precise feedback, guiding the LLM towards correct solutions. This method is like giving LLMs a much-needed math tutor. By catching errors early on, TVM helps LLMs learn from their mistakes and improve their overall mathematical reasoning abilities. Experiments show this approach significantly boosts accuracy on grade-school math problems (GSM8K) and even improves performance on more complex, advanced math problems (MATH dataset). The results are promising, suggesting a future where LLMs can reliably tackle challenging mathematical reasoning tasks. However, this is just the beginning. More research is needed to explore TVM’s potential with larger LLMs and in other complex reasoning tasks beyond mathematics. Could this type of granular feedback revolutionize how LLMs learn, not just in math, but in other areas requiring intricate, step-by-step reasoning?