https://arxiv.org/abs/2402.03300

Abstract

Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which continues pre-training DeepSeek-Coder-Base-v1.5 7B with 120B math-related tokens sourced from Common Crawl, together with natural language and code data. DeepSeekMath 7B has achieved an impressive score of 51.7% on the competition-level MATH benchmark without relying on external toolkits and voting techniques, approaching the performance level of Gemini-Ultra and GPT-4. Self-consistency over 64 samples from DeepSeekMath 7B achieves 60.9% on MATH. The mathematical reasoning capability of DeepSeekMath is attributed to two key factors: First, we harness the significant potential of publicly available web data through a meticulously engineered data selection pipeline. Second, we introduce Group Relative Policy Optimization (GRPO), a variant of Proximal Policy Optimization (PPO), that enhances mathematical reasoning abilities while concurrently optimizing the memory usage of PPO.

https://twitter.com/deepseek_ai/status/1754701472363958581

🚀 DeepSeekMath: Approaching Mathematical Reasoning Capability of GPT-4 with a 7B Model.

Highlights:

Continue pre-training DeepSeek-Coder-Base-v1.5 7B with 120B math tokens from Common Crawl.

Introduce GRPO, a variant of PPO, that enhances mathematical reasoning and reduces training resources.

More Details：https://arxiv.org/abs/2402.03300

Model Download：https://huggingface.co/deepseek-ai

GitHub Repo：https://github.com/deepseek-ai/DeepSeek-Math