刚刚！DeepSeek梁文锋论文登上《Nature》封面了！

Core Viewpoint - The DeepSeek-R1 inference model research paper, led by Liang Wenfeng, has been published in the prestigious journal Nature, marking a significant milestone in the field of AI and large language models [1][3]. Group 1: Model Development and Validation - The latest paper provides more detailed insights into the training of the DeepSeek-R1 model compared to its initial version released in January [3]. - DeepSeek-R1 is recognized as the first mainstream large language model to undergo peer review, addressing previous concerns regarding its distillation process [3]. - The peer review process is seen as a necessary step to mitigate the risks associated with unverified claims in the AI industry, as highlighted by Nature [5]. Group 2: Data and Safety Assessment - DeepSeek-V3 Base, the foundational model for DeepSeek-R1, utilized data sourced entirely from the internet, which may include outputs generated by GPT-4, though this was not intentional [5]. - The company has provided a detailed process in supplementary materials to demonstrate how data contamination was minimized during training, ensuring that benchmark tests were not deliberately included to enhance model performance [5]. - A comprehensive safety assessment of DeepSeek-R1 has been conducted, showing that its safety features are superior to those of contemporaneous models [5].