Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Watch a 34-minute technical video exploring Microsoft Research Asia's rStar-Math framework for developing mathematical reasoning capabilities in Small Language Models (SLMs). Learn how DeepSeek 236B generates high-quality, step-by-step reasoning trajectories for mathematical tasks in Round 1, which are then used to fine-tune a 7B Qwen SLM. Discover the self-evolution framework that employs Monte Carlo Tree Search (MCTS) and Process Preference Model (PPM) to iteratively enhance the smaller policy model's performance. Examine an open research question about whether a pure 7B policy model could achieve high-performance mathematical reasoning through self-evolution techniques like MCTS and code-augmented verification. Based on research by Xinyu Guan, Li Lyna Zhang, Yifei Liu, Ning Shang, Youran Sun, Yi Zhu, Fan Yang, and Mao Yang from Microsoft Research Asia.
