Liang Wenfeng's paper appears on the cover of Nature

On September 17, the research paper "DeepSeek-R1: Structure and Training of an Efficient Inference Model," co-authored by the DeepSeek team and with Liang Wenfeng as corresponding author, officially graced the cover of the top international academic journal Nature, garnering widespread attention.

Compared to the initial version of the paper published in January of this year, this newly published version not only systematically discloses more technical details about model training but also, for the first time, directly addresses initial concerns about knowledge distillation raised during the model's release, further enhancing the transparency and credibility of the research.

Notably, DeepSeek-R1 became the world's first mainstream large-scale language model to pass rigorous independent peer review. Currently, the vast majority of mainstream large-scale models have yet to undergo this type of academic review, a situation that often results in controversy surrounding the reproducibility of the models and the reliability of their conclusions.

Nature noted in its commentary that this gap in peer review "has finally been broken by DeepSeek," fully affirming the research's significance in advancing the scientific and standardized development of large-scale models.

The successful release and recognition of DeepSeek-R1 not only demonstrates the profound strength of the Chinese team in basic research in artificial intelligence, but also provides valuable experience and new ideas for the training methods, evaluation systems, and knowledge transfer mechanisms of large language models.