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The article presents rStar-Math, a method demonstrating that small language models (SLMs) can rival or surpass the math reasoning capabilities of larger models like OpenAI's without distillation. rStar-Math employs Monte Carlo Tree Search (MCTS) for 'deep thinking', using a math policy SLM guided by an SLM-based process reward model. It introduces three innovations: a code-augmented CoT data synthesis method for training the policy SLM, a novel process reward model training method avoiding step-level score annotation, and a self-evolution recipe where both the policy SLM and process preference model are iteratively improved. Through self-evolution with millions of solutions for 747k math problems, rStar-Math achieves state-of-the-art math reasoning, significantly improving performance on benchmarks like MATH and AIME.
Dr Valeria Correa Vaz de Paiva is a Brazilian mathematician, logician, computer scientist, and computational linguist known for introducing the concept of dialectica spaces, which model linear logic in advanced programming languages. She has contributed significantly to natural language processing (NLP) and works at the Topos Institute in Berkeley, California. De Paiva is committed to encouraging more women into STEM fields and is actively involved in Women In Logic (WIL).
PARsing Mathematical Entities Search And Navigation: A tool to search for definitions and usage examples of mathematical concepts.
A map of math articles from ArXiv using t-SNE and nomic-embed.
$$logloss(theta) = - {1 over m} sum_{i=1}^m (y_i ln(hat p(y_i=1)) + (1-y_i) ln(1-hat p(y_i=1)))$$
