2512.02556 Dec 02, 2025 cs.AI

DeepSeek-V3.2: 오픈 거대 언어 모델의 최전선을 확장하다

DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

DeepSeek-AI
DeepSeek-AI
Citations: 8,627
h-index: 4
Dejian Yang
Dejian Yang
Citations: 11,133
h-index: 11
Haowei Zhang
Haowei Zhang
Citations: 8,985
h-index: 8
Jun-Mei Song
Jun-Mei Song
Citations: 14,450
h-index: 10
Ruoyu Zhang
Ruoyu Zhang
Citations: 8,276
h-index: 6
Qihao Zhu
Qihao Zhu
Citations: 16,093
h-index: 11
Shirong Ma
Shirong Ma
Citations: 9,725
h-index: 10
Peiyi Wang
Peiyi Wang
Citations: 8,437
h-index: 6
Xiaoling Bi
Xiaoling Bi
Citations: 8,245
h-index: 4
Xiaokang Zhang
Xiaokang Zhang
Citations: 8,279
h-index: 6
Xingkai Yu
Xingkai Yu
Citations: 10,388
h-index: 10
Yu Wu
Yu Wu
Citations: 8,721
h-index: 5
Z. F. Wu
Z. F. Wu
Citations: 8,546
h-index: 7
Zhibin Gou
Zhibin Gou
Tsinghua University
Citations: 10,696
h-index: 17
Zhuoshu Li
Zhuoshu Li
Citations: 8,958
h-index: 6
Ziyi Gao
Ziyi Gao
Citations: 8,256
h-index: 5
A. Liu
A. Liu
Citations: 9,821
h-index: 8
Bing Xue
Bing Xue
Citations: 8,260
h-index: 5
Bing-Li Wang
Bing-Li Wang
Citations: 10,519
h-index: 9
Bochao Wu
Bochao Wu
Citations: 8,258
h-index: 5
B. Feng
B. Feng
Citations: 8,243
h-index: 4
Chengda Lu
Chengda Lu
Citations: 8,310
h-index: 7
Chenggang Zhao
Chenggang Zhao
Citations: 10,249
h-index: 12
C. Deng
C. Deng
Citations: 10,818
h-index: 11
C. Ruan
C. Ruan
Citations: 12,769
h-index: 14
Damai Dai
Damai Dai
Citations: 9,596
h-index: 11
Deli Chen
Deli Chen
Citations: 10,752
h-index: 8
Dong-Li Ji
Dong-Li Ji
Citations: 8,246
h-index: 4
Erhang Li
Erhang Li
Citations: 8,919
h-index: 5
Fangyun Lin
Fangyun Lin
Citations: 8,923
h-index: 5
Fucong Dai
Fucong Dai
Citations: 8,243
h-index: 4
Guangbo Hao
Guangbo Hao
Citations: 8,935
h-index: 6
Guanting Chen
Guanting Chen
Citations: 10,407
h-index: 7
Guowei Li
Guowei Li
Citations: 8,932
h-index: 6
H. Zhang
H. Zhang
Citations: 8,242
h-index: 4
Hanwei Xu
Hanwei Xu
Citations: 10,053
h-index: 9
Honghui Ding
Honghui Ding
Citations: 8,940
h-index: 6
Huazuo Gao
Huazuo Gao
Citations: 10,946
h-index: 12
Hui Li
Hui Li
Citations: 8,257
h-index: 5
Jianzhong Guo
Jianzhong Guo
Citations: 8,946
h-index: 6
Jiashi Li
Jiashi Li
Citations: 10,103
h-index: 10
JingChang Chen
JingChang Chen
Citations: 8,248
h-index: 4
Jingyang Yuan
Jingyang Yuan
Citations: 8,540
h-index: 7
Junjie Qiu
Junjie Qiu
Citations: 8,941
h-index: 6
Junlong Li
Junlong Li
Citations: 8,274
h-index: 5
J. Ni
J. Ni
Citations: 8,244
h-index: 4
Jian Liang
Jian Liang
Citations: 8,245
h-index: 4
Jin Chen
Jin Chen
Citations: 8,258
h-index: 5
Kai Dong
Kai Dong
Citations: 11,576
h-index: 10
Kaige Gao
Kaige Gao
Citations: 8,920
h-index: 5
Kang Guan
Kang Guan
Citations: 9,815
h-index: 8
Kexin Huang
Kexin Huang
Citations: 8,281
h-index: 8
K. Yu
K. Yu
Citations: 8,250
h-index: 5
Lean Wang
Lean Wang
Citations: 8,537
h-index: 6
Lecong Zhang
Lecong Zhang
Citations: 8,918
h-index: 5
Liang Zhao
Liang Zhao
Citations: 9,100
h-index: 8
Litong Wang
Litong Wang
Citations: 8,280
h-index: 6
Liyue Zhang
Liyue Zhang
Citations: 9,683
h-index: 10
Lei Xu
Lei Xu
Citations: 8,313
h-index: 6
Leyi Xia
Leyi Xia
Citations: 8,242
h-index: 4
Mingchuan Zhang
Mingchuan Zhang
Citations: 13,757
h-index: 7
Minghua Zhang
Minghua Zhang
Citations: 8,246
h-index: 4
Meng Li
Meng Li
Citations: 8,244
h-index: 4
Miaojun Wang
Miaojun Wang
Citations: 8,247
h-index: 4
Mingming Li
Mingming Li
Citations: 8,251
h-index: 5
Ning Tian
Ning Tian
Citations: 8,266
h-index: 6
Peng Zhang
Peng Zhang
Citations: 8,268
h-index: 6
Qiancheng Wang
Qiancheng Wang
Citations: 8,350
h-index: 6
Qinyu Chen
Qinyu Chen
Citations: 8,658
h-index: 6
Qiushi Du
Qiushi Du
Citations: 9,496
h-index: 9
Ruiqi Ge
Ruiqi Ge
Citations: 8,945
h-index: 6
Ruisong Zhang
Ruisong Zhang
Citations: 8,242
h-index: 4
Ruizhe Pan
Ruizhe Pan
Citations: 8,317
h-index: 5
Runji Wang
Runji Wang
Citations: 8,242
h-index: 4
R. J. Chen
R. J. Chen
Citations: 8,242
h-index: 4
R. Jin
R. Jin
Citations: 8,246
h-index: 4
Ruyi Chen
Ruyi Chen
Citations: 8,270
h-index: 6
Shanghao Lu
Shanghao Lu
Citations: 8,945
h-index: 7
Shangyan Zhou
Shangyan Zhou
Citations: 9,007
h-index: 8
Shanhuang Chen
Shanhuang Chen
Citations: 8,939
h-index: 6
Shiyu Wang
Shiyu Wang
Citations: 8,236
h-index: 4
Shuiping Yu
Shuiping Yu
Citations: 8,965
h-index: 7
Shunfeng Zhou
Shunfeng Zhou
Citations: 8,926
h-index: 5
Shuting Pan
Shuting Pan
Citations: 8,230
h-index: 3
S. Li
S. Li
Citations: 8,230
h-index: 3
Shuang Zhou
Shuang Zhou
Citations: 8,298
h-index: 6
Shao-Kang Wu
Shao-Kang Wu
Citations: 5,569
h-index: 3
Tao Yun
Tao Yun
Citations: 8,230
h-index: 3
Tian Pei
Tian Pei
Citations: 8,967
h-index: 7
T. Sun
T. Sun
Citations: 8,243
h-index: 4
Wangding Zeng
Wangding Zeng
Citations: 9,637
h-index: 9
Wen Liu
Wen Liu
Citations: 9,688
h-index: 8
W. Liang
W. Liang
Citations: 11,855
h-index: 12
Wenjun Gao
Wenjun Gao
Citations: 9,644
h-index: 10
Wentao Zhang
Wentao Zhang
Citations: 5,608
h-index: 4
Wei An
Wei An
Citations: 8,263
h-index: 5
Xiaodong Liu
Xiaodong Liu
Citations: 8,974
h-index: 7
Xiaohan Wang
Xiaohan Wang
Citations: 8,249
h-index: 5
Xiaokang Chen
Xiaokang Chen
Citations: 9,703
h-index: 9
X. Nie
X. Nie
Citations: 8,950
h-index: 7
Xin Xie
Xin Xie
Citations: 9,816
h-index: 8
Xingchao Liu
Xingchao Liu
Citations: 9,758
h-index: 9
Xinyu Yang
Xinyu Yang
Citations: 8,244
h-index: 4
Xinyuan Li
Xinyuan Li
Citations: 8,242
h-index: 4
Xuecheng Su
Xuecheng Su
Citations: 8,944
h-index: 6
Xuheng Lin
Xuheng Lin
Citations: 8,242
h-index: 4
X. Q. Li
X. Q. Li
Citations: 8,230
h-index: 3
Xiangyu Jin
Xiangyu Jin
Citations: 8,379
h-index: 6
Xiaosha Chen
Xiaosha Chen
Citations: 8,329
h-index: 5
Xiaowen Sun
Xiaowen Sun
Citations: 8,250
h-index: 4
Xinnan Song
Xinnan Song
Citations: 8,230
h-index: 3
Xinyi Zhou
Xinyi Zhou
Citations: 8,236
h-index: 3
Xianzu Wang
Xianzu Wang
Citations: 8,242
h-index: 4
Y. Q. Wang
Y. Q. Wang
Citations: 8,230
h-index: 3
Yanhong Xu
Yanhong Xu
Citations: 8,927
h-index: 5
Yao Li
Yao Li
Citations: 17,134
h-index: 7
Yao Zhao
Yao Zhao
Citations: 8,930
h-index: 5
Yaofeng Sun
Yaofeng Sun
Citations: 10,176
h-index: 8
Yaohui Wang
Yaohui Wang
Citations: 9,388
h-index: 7
Yichao Zhang
Yichao Zhang
Citations: 8,263
h-index: 5
Yifan Shi
Yifan Shi
Citations: 8,230
h-index: 3
Yi Xiong
Yi Xiong
Citations: 8,277
h-index: 5
Ying He
Ying He
Citations: 8,322
h-index: 6
Y. Piao
Y. Piao
Citations: 9,809
h-index: 7
Yisong Wang
Yisong Wang
Citations: 8,833
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Yixuan Tan
Yixuan Tan
Citations: 8,268
h-index: 5
Yiyang Ma
Yiyang Ma
Citations: 5,555
h-index: 2
Yiyuan Liu
Yiyuan Liu
Citations: 8,926
h-index: 5
Yongqiang Guo
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h-index: 3
Yuduan Wang
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h-index: 3
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h-index: 6
Yunfan Xiong
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Citations: 8,300
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h-index: 5
Yu-mei You
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Yuxuan Liu
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Yuyang Zhou
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h-index: 3
Y. X. Zhu
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Yanping Huang
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h-index: 5
Yuchen Zhu
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Yunxiang Ma
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Ying Tang
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Zhangli Sha
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Zhe Fu
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Citations: 9,087
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Zhean Xu
Zhean Xu
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Zhenda Xie
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Citations: 14,098
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Zhen-guo Zhang
Zhen-guo Zhang
Citations: 8,240
h-index: 4
Zhewen Hao
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Citations: 9,322
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Zhicheng Ma
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Citations: 8,230
h-index: 3
Zhigang Yan
Zhigang Yan
Citations: 8,227
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Zhiyu Wu
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Citations: 9,690
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Zihui Gu
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Citations: 8,656
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Zijia Zhu
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Citations: 8,227
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Ziwei Xie
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Zizheng Pan
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Citations: 9,668
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Zhen Huang
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Zhipeng Xu
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Zhongyu Zhang
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Zhen Zhang
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Chenhao Xu
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우리는 높은 계산 효율성과 뛰어난 추론 및 에이전트 성능을 조화시킨 모델인 DeepSeek-V3.2를 소개합니다. DeepSeek-V3.2의 주요 기술적 혁신은 다음과 같습니다. (1) DeepSeek 희소 어텐션(DSA): 긴 문맥 시나리오에서 모델 성능을 유지하면서 계산 복잡도를 대폭 줄이는 효율적인 어텐션 메커니즘인 DSA를 도입합니다. (2) 확장 가능한 강화 학습 프레임워크: 강력한 강화 학습 프로토콜을 구현하고 사후 학습(post-training) 연산량을 확장함으로써, DeepSeek-V3.2는 GPT-5와 대등한 성능을 발휘합니다. 특히 고연산 변형 모델인 DeepSeek-V3.2-Speciale는 GPT-5를 능가하며 Gemini-3.0-Pro와 동등한 수준의 추론 능력을 보여주어, 2025 국제수학올림피아드(IMO)와 국제정보올림피아드(IOI)에서 모두 금메달급 성적을 달성했습니다. (3) 대규모 에이전트 작업 합성 파이프라인: 추론을 도구 사용 시나리오에 통합하기 위해 훈련 데이터를 대규모로 체계적으로 생성하는 새로운 합성 파이프라인을 개발했습니다. 이 방법론은 확장 가능한 에이전트 사후 학습을 촉진하여 복잡한 상호작용 환경에서 일반화 및 지시 이행의 견고성을 크게 향상시킵니다.

Original Abstract

We introduce DeepSeek-V3.2, a model that harmonizes high computational efficiency with superior reasoning and agent performance. The key technical breakthroughs of DeepSeek-V3.2 are as follows: (1) DeepSeek Sparse Attention (DSA): We introduce DSA, an efficient attention mechanism that substantially reduces computational complexity while preserving model performance in long-context scenarios. (2) Scalable Reinforcement Learning Framework: By implementing a robust reinforcement learning protocol and scaling post-training compute, DeepSeek-V3.2 performs comparably to GPT-5. Notably, our high-compute variant, DeepSeek-V3.2-Speciale, surpasses GPT-5 and exhibits reasoning proficiency on par with Gemini-3.0-Pro, achieving gold-medal performance in both the 2025 International Mathematical Olympiad (IMO) and the International Olympiad in Informatics (IOI). (3) Large-Scale Agentic Task Synthesis Pipeline: To integrate reasoning into tool-use scenarios, we developed a novel synthesis pipeline that systematically generates training data at scale. This methodology facilitates scalable agentic post-training, yielding substantial improvements in generalization and instruction-following robustness within complex, interactive environments.

222 Citations
26 Influential
9.5 Altmetric
321.5 Score

AI Analysis

Korean Summary

DeepSeek-V3.2는 높은 연산 효율성과 뛰어난 추론 및 에이전트 성능을 결합한 오픈 소스 대규모 언어 모델입니다. 이 모델은 긴 문맥 처리 시 연산 복잡도를 줄이기 위해 DeepSeek Sparse Attention(DSA) 메커니즘을 도입했으며, 사전 학습 비용의 10% 이상을 할당하는 대규모 강화 학습(RL) 프레임워크를 통해 성능을 극대화했습니다. 또한, 추론 능력을 도구 사용 시나리오에 통합하기 위해 대규모 에이전트 작업 합성 파이프라인을 개발했습니다. 특히 고성능 변형인 DeepSeek-V3.2-Speciale는 국제 수학 올림피아드(IMO)와 정보 올림피아드(IOI)에서 금메달 수준의 성과를 달성하며 GPT-5를 능가하고 Gemini-3.0-Pro와 대등한 성능을 입증했습니다.

Key Innovations

  • DeepSeek Sparse Attention (DSA): 긴 문맥 시나리오에서 성능 저하 없이 연산 복잡도를 대폭 줄이는 효율적인 어텐션 메커니즘
  • 확장 가능한 강화 학습(RL) 프레임워크: Unbiased KL 추정 및 Off-Policy 시퀀스 마스킹 등을 통해 학습 안정성을 확보하고 포스트 트레이닝 연산을 대폭 확장
  • 대규모 에이전트 작업 합성 파이프라인: 1,800개 이상의 환경과 85,000개의 프롬프트를 합성하여 에이전트의 일반화 및 지시 이행 능력 향상
  • 도구 사용 내 생각(Thinking) 통합: 'Cold-Start' 방식과 컨텍스트 관리 전략을 통해 추론 과정과 도구 호출을 효과적으로 결합
  • DeepSeek-V3.2-Speciale: 추론 길이 제약을 완화하고 순수 추론 데이터로 학습하여 최상위권 벤치마크 성능을 달성한 고성능 변형 모델

Learning & Inference Impact

추론(Inference) 단계에서는 DSA를 통해 어텐션 연산의 복잡도를 O(L^2)에서 O(Lk)로 줄여, 긴 시퀀스(128K) 처리 시 비용과 지연 시간을 획기적으로 단축했습니다. 학습(Learning) 단계에서는 GRPO 알고리즘을 개선(Unbiased KL Estimate, Off-Policy Masking 등)하여 대규모 RL 학습 시 발생하는 불안정성을 해결하고, 모델이 자체 생성한 데이터와 합성된 에이전트 데이터를 통해 도구 사용 및 복잡한 추론 능력을 강화했습니다. 이를 통해 오픈 소스 모델임에도 불구하고 독점적 최첨단 모델들과의 성능 격차를 크게 좁히거나 능가하는 결과를 얻었습니다.

Technical Difficulty

고급

Estimated implementation complexity based on methodology.

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