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arxiv: 2406.11931 · v1 · submitted 2024-06-17 · 💻 cs.SE · cs.AI· cs.LG

Recognition: 2 theorem links

· Lean Theorem

DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence

DeepSeek-AI , Qihao Zhu , Daya Guo , Zhihong Shao , Dejian Yang , Peiyi Wang , Runxin Xu , Y. Wu
show 32 more authors
Yukun Li Huazuo Gao Shirong Ma Wangding Zeng Xiao Bi Zihui Gu Hanwei Xu Damai Dai Kai Dong Liyue Zhang Yishi Piao Zhibin Gou Zhenda Xie Zhewen Hao Bingxuan Wang Junxiao Song Deli Chen Xin Xie Kang Guan Yuxiang You Aixin Liu Qiushi Du Wenjun Gao Xuan Lu Qinyu Chen Yaohui Wang Chengqi Deng Jiashi Li Chenggang Zhao Chong Ruan Fuli Luo Wenfeng Liang
Authors on Pith no claims yet

Pith reviewed 2026-05-16 00:55 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.LG
keywords code language modelmixture of expertsopen-source modelcoding benchmarksmathematical reasoningprogramming languagescontext length
0
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The pith

An open-source code model matches or exceeds closed-source leaders on coding and math benchmarks after training on six trillion extra tokens.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents DeepSeek-Coder-V2 as an open-source Mixture-of-Experts model that closes the performance gap with proprietary systems on code-related work. It starts from an earlier checkpoint and adds six trillion tokens of continued pre-training focused on code and math. The result is stronger reasoning on coding tasks, support for 338 programming languages instead of 86, and context length raised to 128K tokens. The authors position this as evidence that open models can deliver competitive code intelligence without closed-source restrictions.

Core claim

DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 using an additional six trillion tokens. This step substantially boosts its coding and mathematical reasoning while preserving general language performance. The model expands language coverage from 86 to 338 and context length from 16K to 128K. On standard benchmarks it records higher scores than GPT-4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math evaluations.

What carries the argument

Continued pre-training on a large code and math corpus using a Mixture-of-Experts architecture that activates only a subset of parameters during inference.

Load-bearing premise

The reported benchmark scores reflect genuine new capability rather than overlap between the training data and the test problems.

What would settle it

Running the model on a fresh set of coding problems created after the training data cutoff and comparing results against the published scores would test whether the gains hold on unseen tasks.

read the original abstract

We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K. In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents DeepSeek-Coder-V2, an open-source Mixture-of-Experts code language model obtained via continued pre-training of an intermediate DeepSeek-V2 checkpoint on an additional 6 trillion tokens. It claims substantial gains in coding and mathematical reasoning over the prior DeepSeek-Coder-33B, expansion from 86 to 338 programming languages and from 16K to 128K context length, maintenance of general-language performance, and superior results relative to closed-source models (GPT-4-Turbo, Claude 3 Opus, Gemini 1.5 Pro) on coding and math benchmarks.

Significance. If the benchmark superiority claims are substantiated by rigorous decontamination and statistical controls, the work would be significant: it would supply the first openly available model that matches or exceeds the leading closed-source systems on code intelligence tasks, thereby lowering barriers to reproducible research in software engineering and AI-assisted programming.

major comments (2)
  1. [Abstract] Abstract: the headline claim of superiority over GPT-4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro on coding and math benchmarks is presented without any description of the evaluation protocol, the precise benchmark suite (HumanEval, MBPP, GSM8K, MATH, etc.), decontamination steps, or statistical significance tests, rendering the central empirical result only moderately supported.
  2. [Abstract / Training section] The description of continued pre-training on 6 T tokens supplies no overlap statistics, membership-inference results, or ablation that removes any examples overlapping the test prompts; in the absence of such checks the observed margins cannot be confidently attributed to generalization rather than leakage.
minor comments (2)
  1. [Abstract] The abstract states performance is 'comparable' on general language tasks but does not quantify the degradation or improvement relative to the base DeepSeek-V2 checkpoint.
  2. [Abstract] No table or figure is referenced that would allow direct comparison of the new model's scores against the closed-source baselines on each individual benchmark.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract and training details. We address each major comment below and have made revisions to strengthen the presentation of our results.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim of superiority over GPT-4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro on coding and math benchmarks is presented without any description of the evaluation protocol, the precise benchmark suite (HumanEval, MBPP, GSM8K, MATH, etc.), decontamination steps, or statistical significance tests, rendering the central empirical result only moderately supported.

    Authors: We agree the abstract is concise by design and omits protocol details. The full evaluation protocol, benchmark definitions (HumanEval, MBPP, GSM8K, MATH and others), decontamination steps, and statistical comparisons appear in Sections 4 and 5. We have revised the abstract to briefly name the primary coding and math benchmarks and to direct readers to the main text for the complete protocol and significance testing. revision: yes

  2. Referee: [Abstract / Training section] The description of continued pre-training on 6 T tokens supplies no overlap statistics, membership-inference results, or ablation that removes any examples overlapping the test prompts; in the absence of such checks the observed margins cannot be confidently attributed to generalization rather than leakage.

    Authors: We acknowledge that explicit decontamination evidence was not provided in the original training description. We have added a dedicated paragraph in the Training section that reports n-gram overlap statistics between the 6 T token corpus and the test sets of the reported benchmarks, together with an ablation that measures performance after removing any overlapping examples. These additions support that the observed gains reflect generalization rather than leakage. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper presents an empirical model, DeepSeek-Coder-V2, trained by continued pre-training on 6 trillion tokens from DeepSeek-V2. Its claims of superior performance are based on direct evaluations against closed-source models on standard benchmarks such as coding and math tasks. There are no mathematical derivations, self-definitional constructs, fitted inputs presented as predictions, or load-bearing self-citations that reduce the results to the inputs by construction. The analysis is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard transformer and MoE assumptions plus the empirical premise that continued pre-training on code data improves domain performance without degrading general capabilities.

axioms (2)
  • standard math Transformer-based MoE architecture improves efficiency for large-scale language modeling
    Invoked implicitly as the base for DeepSeek-V2 continuation
  • domain assumption Additional domain-specific pre-training enhances coding and math reasoning while preserving general language performance
    Core premise of the continued pre-training step

pith-pipeline@v0.9.0 · 5649 in / 1301 out tokens · 76177 ms · 2026-05-16T00:55:13.404239+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · cited by 18 Pith papers · 17 internal anchors

  1. [1]

    L. B. Allal, R. Li, D. Kocetkov, C. Mou, C. Akiki, C. M. Ferrandis, N. Muennighoff, M. Mishra, A. Gu, M. Dey, et al. Santacoder: don’t reach for the stars! arXiv preprint arXiv:2301.03988,

    work page arXiv

  2. [2]

    Program Synthesis with Large Language Models

    J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. Cai, M. Terry, Q. Le, and C. Sutton. Program synthesis with large language models, 2021a. J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. Cai, M. Terry, Q. Le, et al. Program synthesis with large language models. arXiv preprint arXiv:2108.07732, 202...

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P . d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374,

    work page internal anchor Pith review Pith/arXiv arXiv

  4. [5]

    URL http://arxiv.org/abs/1803.05457. K. Cobbe, V . Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168,

    work page internal anchor Pith review Pith/arXiv arXiv

  5. [6]

    Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

    Y. Dubois, B. Galambosi, P . Liang, and T. B. Hashimoto. Length-controlled alpacaeval: A simple way to debias automatic evaluators. arXiv preprint arXiv:2404.04475,

    work page internal anchor Pith review Pith/arXiv arXiv

  6. [7]

    15 D. Guo, Q. Zhu, D. Yang, Z. Xie, K. Dong, W. Zhang, G. Chen, X. Bi, Y. Wu, Y. Li, et al. Deepseek- coder: When the large language model meets programming–the rise of code intelligence. arXiv preprint arXiv:2401.14196,

    work page internal anchor Pith review Pith/arXiv arXiv

  7. [8]

    Measuring Massive Multitask Language Understanding

    D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300,

    work page internal anchor Pith review Pith/arXiv arXiv 2009

  8. [9]

    Measuring Mathematical Problem Solving With the MATH Dataset

    D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt. Mea- suring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103.03874,

    work page internal anchor Pith review Pith/arXiv arXiv

  9. [10]

    Huang, Y

    Y. Huang, Y. Bai, Z. Zhu, J. Zhang, J. Zhang, T. Su, J. Liu, C. Lv, Y. Zhang, J. Lei, et al. C-Eval: A multi-level multi-discipline chinese evaluation suite for foundation models. arXiv preprint arXiv:2305.08322,

    work page arXiv

  10. [11]

    C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan. Swe-bench: Can language models resolve real-world github issues? arXiv preprint arXiv:2310.06770,

    work page internal anchor Pith review Pith/arXiv arXiv

  11. [12]

    FastText.zip: Compressing text classification models

    Association for Computational Linguistics. doi: 10.18653/v1/P17-1147. URL https://aclanthology.org/P17-1147. A. Joulin, E. Grave, P . Bojanowski, M. Douze, H. Jégou, and T. Mikolov. Fasttext. zip: Compress- ing text classification models. arXiv preprint arXiv:1612.03651,

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/p17-1147

  12. [13]

    emnlp-main.1742/

    doi: 10.1162/tacl\_a\_00276. URL https://doi.org/10.1162/tacl_a_00276. H. Li, Y. Zhang, F. Koto, Y. Yang, H. Zhao, Y. Gong, N. Duan, and T. Baldwin. CMMLU: Measur- ing massive multitask language understanding in Chinese. arXiv preprint arXiv:2306.09212, 2023a. R. Li, L. B. Allal, Y. Zi, N. Muennighoff, D. Kocetkov, C. Mou, M. Marone, C. Akiki, J. Li, J. C...

    work page internal anchor Pith review doi:10.1162/tacl

  13. [14]

    URL https://lmsys.org/ blog/2024-04-19-arena-hard/ . J. Liu, C. S. Xia, Y. Wang, and L. Zhang. Is your code generated by chatGPT really correct? rigor- ous evaluation of large language models for code generation. In Thirty-seventh Conference 16 on Neural Information Processing Systems, 2023a. URL https://openreview.net/for um?id=1qvx610Cu7. T. Liu, C. Xu,...

    work page doi:10.48550/a 2024

  14. [15]

    StarCoder 2 and The Stack v2: The Next Generation

    A. Lozhkov, R. Li, L. B. Allal, F. Cassano, J. Lamy-Poirier, N. Tazi, A. Tang, D. Pykhtar, J. Liu, Y. Wei, et al. Starcoder 2 and the stack v2: The next generation. arXiv preprint arXiv:2402.19173,

    work page internal anchor Pith review Pith/arXiv arXiv

  15. [16]

    American invitational mathematics examination - aime

    MAA. American invitational mathematics examination - aime. American Invitational Mathematics Examination - AIME 2024,

    work page 2024

  16. [17]

    Netmind.AI

    Accessed: 2024-05-29. Netmind.AI. Odyssey-math. https://github.com/protagolabs/odyssey-math/tree /main,

    work page 2024

  17. [18]

    B. Peng, J. Quesnelle, H. Fan, and E. Shippole. Yarn: Efficient context window extension of large language models. arXiv preprint arXiv:2309.00071,

    work page internal anchor Pith review Pith/arXiv arXiv

  18. [19]

    M. Reid, N. Savinov, D. Teplyashin, D. Lepikhin, T. Lillicrap, J.-b. Alayrac, R. Soricut, A. Lazari- dou, O. Firat, J. Schrittwieser, et al. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530,

    work page internal anchor Pith review Pith/arXiv arXiv

  19. [20]

    Code Llama: Open Foundation Models for Code

    B. Roziere, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin, et al. Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950,

    work page internal anchor Pith review Pith/arXiv arXiv

  20. [21]

    Z. Shao, P . Wang, Q. Zhu, R. Xu, J. Song, M. Zhang, Y. Li, Y. Wu, and D. Guo. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300,

    work page internal anchor Pith review Pith/arXiv arXiv

  21. [22]

    Q. Shi, M. Tang, K. Narasimhan, and S. Yao. Can language models solve olympiad programming? arXiv preprint arXiv:2404.10952,

    work page arXiv

  22. [23]

    Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

    M. Suzgun, N. Scales, N. Schärli, S. Gehrmann, Y. Tay, H. W. Chung, A. Chowdhery, Q. V . Le, E. H. Chi, D. Zhou, et al. Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261,

    work page internal anchor Pith review Pith/arXiv arXiv

  23. [24]

    Llama 2: Open Foundation and Fine-Tuned Chat Models

    H. Touvron, L. Martin, K. Stone, P . Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P . Bhargava, S. Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models.arXiv preprint arXiv:2307.09288,

    work page internal anchor Pith review Pith/arXiv arXiv

  24. [25]

    17 L. Xu, H. Hu, X. Zhang, L. Li, C. Cao, Y. Li, Y. Xu, K. Sun, D. Yu, C. Yu, Y. Tian, Q. Dong, W. Liu, B. Shi, Y. Cui, J. Li, J. Zeng, R. Wang, W. Xie, Y. Li, Y. Patterson, Z. Tian, Y. Zhang, H. Zhou, S. Liu, Z. Zhao, Q. Zhao, C. Yue, X. Zhang, Z. Yang, K. Richardson, and Z. Lan. CLUE: A chi- nese language understanding evaluation benchmark. In D. Scott,...

    work page 2020

  25. [26]

    URL https://doi.org/10.18653/v1/2020.coling-main.419

    doi: 10.18653/V1/2020.COLING-MAIN.419. URL https://doi.org/10.18653/v1/2020.coling-main.419. L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. P . Xing, H. Zhang, J. E. Gonzalez, and I. Stoica. Judging llm-as-a-judge with mt-bench and chatbot arena,

    work page doi:10.18653/v1/2020.coling-main.419 2020

  26. [27]

    Zhong, R

    W. Zhong, R. Cui, Y. Guo, Y. Liang, S. Lu, Y. Wang, A. Saied, W. Chen, and N. Duan. AGIEval: A human-centric benchmark for evaluating foundation models. CoRR, abs/2304.06364,

    work page arXiv

  27. [28]

    Zhong, R

    doi: 10.48550/arXiv.2304.06364. URL https://doi.org/10.48550/arXiv.2304.06364. 18 A. Supported Programming Languages ABAP , ActionScript, Ada, Agda, AGS Script, Alloy, AmbientTalk, AMD GPU, AMPL, ANSYS Parametric Design Language, ANTLR, Apache Configuration, APL, AppleScript, Arc, Arduino, ASP , AspectJ, Assembly, Asymptote, Augeas, AutoHotkey, AutoIt, AW...

    work page doi:10.48550/arxiv.2304.06364