Large language models (LLMs) have made impressive strides in various tasks, but complex mathematical reasoning remains a challenge. Existing methods often rely on expensive and time-consuming process supervision from domain experts or tools like GPT-4. Imagine if LLMs could learn math on their own, like humans iteratively refining their problem-solving skills. Researchers have introduced AlphaMath, a novel framework that allows LLMs to bootstrap their mathematical reasoning abilities *without* explicit process annotations. AlphaMath leverages Monte Carlo Tree Search (MCTS), a powerful search algorithm used in game AI, to explore different reasoning paths. It combines the LLM with a value model, which learns to assess the quality of intermediate reasoning steps, guiding the LLM toward more effective solutions. This mimics how humans learn from both correct and incorrect steps during problem-solving. During inference, a clever technique called step-level beam search helps the LLM navigate the solution space efficiently, making it practical for real-world applications. Experiments show that AlphaMath achieves results comparable to or better than state-of-the-art methods, even without human-annotated solutions. This opens exciting possibilities for LLMs to learn complex reasoning autonomously, potentially revolutionizing how we approach problem-solving in AI. Future research aims to make AlphaMath completely independent of human-provided answers, creating a truly self-learning system. This could lead to a closed-loop self-evolution framework where LLMs continuously learn and improve their reasoning abilities by exploring and evaluating different approaches, paving the way for more robust and intelligent AI systems.