DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open
  Language Models
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.
65c19a1f55c4f06fa9692c52
Zhihong Shao
claimed_verified
2025-01-26T11:41:46.616Z
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Zhihong Shao
user
ZhihongShao
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Peiyi Wang
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2024-02-13T08:13:15.437Z
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PEIYI, WANG
user
peiyiwang89
65c19a1f55c4f06fa9692c54
Qihao Zhu
admin_assigned
2024-02-13T08:13:25.841Z
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zhuqihao
user
zqh11
65c19a1f55c4f06fa9692c55
Runxin Xu
65c19a1f55c4f06fa9692c56
Junxiao Song
admin_assigned
2024-02-13T08:13:39.870Z
6565a2dd131d13ccc5d8cb12
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Junxiao Song
user
haha-point
65c19a1f55c4f06fa9692c57
Mingchuan Zhang
65c19a1f55c4f06fa9692c58
Y. K. Li
65c19a1f55c4f06fa9692c59
Y. Wu
65c19a1f55c4f06fa9692c5a
Daya Guo
admin_assigned
2024-02-13T08:14:02.332Z
653df20eaa1f487614da4db1
/avatars/12b27ce2c59f53b7e464039deab36a5d.svg
Daya Guo
user
guoday
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open
  Language Models
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.
DeepSeekMath 7B improves mathematical reasoning through enhanced data pre-training and Group Relative Policy Optimization, achieving high scores on MATH benchmark without external tools.
DeepSeekMath 7B DeepSeek-Coder-Base-v1.5 7B MATH benchmark Group Relative Policy Optimization Proximal Policy Optimization mathematical reasoning data selection pipeline