This podcast episode analyzes a research paper on DeepSeek Math, a large language model designed for solving mathematical problems. The speaker details the paper's two-pronged approach: creating a massive, high-quality dataset from Common Crawl through an iterative process, and employing a novel reinforcement learning algorithm called GRPO to optimize the model's performance. DeepSeek Math achieves state-of-the-art results on various math benchmarks, even outperforming larger commercial models in some cases. The analysis highlights the effectiveness of the data collection method and the advantages of GRPO, which eliminates the need for a separate value model in reinforcement learning. The speaker concludes by discussing the limitations of solely relying on fine-tuning and reinforcement learning to achieve Artificial General Intelligence (AGI).
