Recently, the DeepSeek team's research achievement "DeepSeek R1" successfully made it to the cover of the prestigious international academic journal "Nature," becoming the first large language model to pass authoritative peer review. This milestone not only marks a significant breakthrough for DeepSeek in the field of AI but also provides a new direction for future AI research.

The editorial board of "Nature" pointed out that in the context of rapid development in AI technology, many technical claims lack transparency and verifiability. The success of DeepSeek proves that through strict independent peer review, the transparency and reproducibility of AI research can be effectively improved, thereby reducing potential social risks. Editors called on more AI companies to follow DeepSeek's example and jointly promote the healthy development of the industry.

In the paper, DeepSeek R1 detailed its unique training method for reasoning capabilities. Unlike traditional methods that rely on manually annotated examples, DeepSeek R1 does not use any human examples at all. Instead, it self-evolves in an autonomous environment through reinforcement learning (RL), thereby developing complex reasoning abilities. This innovative training approach has achieved remarkable results. In the AIME2024 math competition, the performance of DeepSeek R1 increased from 15.6% to 71.0%, reaching a level comparable to that of OpenAI models.

During the months-long peer review process, experts provided valuable feedback, which prompted the DeepSeek team to make multiple revisions and improvements to the technical details. Although the research findings are impressive, the team also honestly acknowledged challenges such as readability and language mixing. To further enhance the model's writing ability and overall performance, DeepSeek adopted a multi-stage training framework combining rejection sampling with supervised fine-tuning.

The successful publication of DeepSeek R1 marks that research on AI foundation models is moving toward a more scientific, rigorous, and reproducible direction. This important breakthrough not only sets an example for future AI research but also has the potential to drive the entire industry toward a more transparent and open development path.