DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914773210234880 |
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| author | Shao, Zhihong Wang, Peiyi Zhu, Qihao Xu, Runxin Song, Junxiao Bi, Xiao Zhang, Haowei Zhang, Mingchuan Li, Y. K. Wu, Y. Guo, Daya |
| author_facet | Shao, Zhihong Wang, Peiyi Zhu, Qihao Xu, Runxin Song, Junxiao Bi, Xiao Zhang, Haowei Zhang, Mingchuan Li, Y. K. Wu, Y. Guo, Daya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03300 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models Shao, Zhihong Wang, Peiyi Zhu, Qihao Xu, Runxin Song, Junxiao Bi, Xiao Zhang, Haowei Zhang, Mingchuan Li, Y. K. Wu, Y. Guo, Daya Computation and Language Artificial Intelligence Machine Learning 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. |
| title | DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.03300 |