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DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models

Paper · 2025 · Luolin Xiong, Haofen Wang, Xi Chen et al.

Summary

This survey, written by researchers at several Chinese universities and Swinburne University, explains why DeepSeek's V3 and R1 models drew so much attention and what they suggest about the direction of large AI models. It is a review, not an original experiment. The authors first trace language modeling from statistical approaches (HMMs, N-grams) through embeddings and RNN/LSTM models to Transformers, pretrained LLMs and recent reasoning models. They then contrast the mainstream LLM paradigm with DeepSeek's approach. The core of the paper covers DeepSeek's algorithmic contributions: MLA, DeepSeekMoE, MTP and GRPO. It also describes engineering optimizations for scaling, training, inference and system architecture, such as FP8 mixed-precision training. Finally, it compares DeepSeek with other leading LLMs in several application areas and discusses challenges and future trends in data, training and reasoning. The authors' main argument is that DeepSeek moved the field away from simply adding parameters and compute. Instead, it showed that careful algorithms and high-quality data can deliver near-frontier performance at much lower cost in open-weights models.

Key points

  • DeepSeek-V3 (December 2024): a MoE model with 671B total parameters, of which 37B are active per token. It has a 128K-token context window and was trained on 14.8T tokens.
  • DeepSeek-R1-Zero: improves reasoning with large-scale GRPO reinforcement learning alone, without SFT or model-based rewards. During training it showed an "Aha Moment," learning on its own to revisit and rethink its initial approach.
  • DeepSeek-R1: fixes R1-Zero's language mixing and poor readability. It adds cold-start training on curated data, then SFT on reasoning and non-reasoning synthetic data, and only then RL. Its reasoning is reported as comparable to OpenAI o1.
  • Market impact: both R1 models were released on January 20, 2025. By January 27, the R1-based app was the most-downloaded free app on the US iOS App Store, ahead of ChatGPT, and Nvidia's share price fell about 17%.
  • Model lineage (Table I): DeepSeek-LLM V1 (7B/67B, 2T tokens), DeepSeek-MoE (16B), DeepSeekMath (1.3B as listed in the table, 150B tokens) and DeepSeek-V2 (236B, 21B active, 8.1T tokens). Open-source infrastructure projects cited include DeepGEMM, FlashMLA and DeepEP.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

 Fields, topics and concepts

Cite

Cite as Luolin Xiong, Haofen Wang, Xi Chen, Lu Sheng, Yun Xiong, Jingping Liu, Yanghua Xiao, Huajun Chen, Qing-Long Han and Yang Tang. 2025. DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models. arXiv:2507.09955.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2507.09955 · arXiv 2507.09955

@misc{xiong2025deepseek,
  title = {{DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models}},
  author = {Luolin Xiong, Haofen Wang, Xi Chen, Lu Sheng, Yun Xiong, Jingping Liu, Yanghua Xiao, Huajun Chen, Qing-Long Han and Yang Tang},
  year = {2025},
  doi = {10.48550/arxiv.2507.09955},
  eprint = {2507.09955},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2507.09955}
}

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