rStar2-Agent: Breakthrough 14B AI Model Outperforms 671B Giants in Math Reasoning

rStar2-Agent: How a 14B Model Achieves Frontier Math Reasoning with Agentic Reinforcement Learning Introduction In the rapidly evolving field of artificial intelligence, large language models (LLMs) have made impressive strides in complex reasoning tasks. However, many state-of-the-art models rely on extensive computational resources and lengthy “chain-of-thought” (CoT) processes that essentially encourage models to “think longer” rather than “think smarter.” A groundbreaking technical report from Microsoft Research introduces rStar2-Agent, a 14-billion-parameter math reasoning model that challenges this paradigm. Through innovative agentic reinforcement learning techniques, this compact model achieves performance comparable to giants like the 671-billion-parameter DeepSeek-R1, demonstrating that smarter training methodologies …