According to a report from Bijie.com, the DeepSeek-R1 paper was featured as the cover article in Nature, with DeepSeek founder and CEO Liang Wenfeng as the corresponding author. The research team demonstrated through experiments that the reasoning capabilities of large language models can be improved through pure reinforcement learning, reducing the workload for human input and outperforming models trained with traditional methods on tasks such as mathematics and programming. DeepSeek-R1 has 91.1k stars on GitHub and has received positive reviews from developers worldwide. An assistant professor at Carnegie Mellon University and others have praised its evolution from a powerful but opaque solution finder to a system capable of human-like conversation. In an editorial, Nature recognized it as the first mainstream LLM published after peer review, a welcome step towards transparency. Peer review helps clarify the workings of LLMs, assess their effectiveness, and improve model security.