DeepSeekMath - Pushing the Limits of Mathematical Reasoning in Open Language Models

Explore a detailed analysis of a research paper that introduces DeepSeekMath 7B, a groundbreaking language model specifically designed for mathematical reasoning. Learn how this 7B parameter model achieves remarkable performance on complex mathematical benchmarks through innovative techniques like Group Relative Policy Optimization (GRPO) and specialized data selection. Understand the model's architecture, which builds upon DeepSeek-Coder-Base-v1.5 and utilizes 120B math-related tokens from Common Crawl, alongside natural language and code data. Discover how this model approaches the performance levels of larger models like Gemini-Ultra and GPT-4 on the competition-level MATH benchmark, achieving a 51.7% score without external toolkits and reaching 60.9% with self-consistency over 64 samples. Delve into the technical details of GRPO, a variant of Proximal Policy Optimization (PPO), and how it optimizes both mathematical reasoning abilities and memory usage.