On September 18, the field of large language models (LLMs) witnessed a milestone breakthrough. The DeepSeek team successfully made the cover of the prestigious academic journal "Nature" with their research paper on DeepSeek R1, becoming the first large language model to pass authoritative peer review. This event not only demonstrates the technological innovation of DeepSeek R1 but also sets a new academic standard for the entire AI industry.
The editorial board of "Nature" pointed out that in the current context of rapid development and excessive hype in AI technology, DeepSeek's approach provides an effective strategy for the industry. By implementing strict independent peer review, the transparency and reproducibility of AI research have been improved, thereby reducing the social risks that may arise from unverified technical claims. The editors called on more AI companies to follow DeepSeek's example to promote the healthy development of the AI field.
This paper details the innovative reasoning ability training method of DeepSeek R1. Unlike traditional methods that rely on manual annotation for fine-tuning, this model does not use any human examples but instead evolves autonomously in an environment through reinforcement learning (RL), developing complex reasoning abilities. This approach has achieved significant results, such as DeepSeek-R1's performance in the AIME2024 math competition jumping from 15.6% to 71.0%, reaching a level comparable to OpenAI models.
Schematic diagram of the group relative policy optimization algorithm (source: DeepSeek)
During the months-long peer review process, eight experts provided valuable suggestions, prompting the DeepSeek team to make multiple revisions and improvements to the technical details. Although the research results are significant, the team also admitted that the model still faces challenges in readability and language mixing. To address these issues, DeepSeek adopted a multi-stage training framework combining rejection sampling and supervised fine-tuning, further enhancing the model's writing capabilities and overall performance.
The successful publication of DeepSeek R1 marks a step forward in AI foundation model research toward a more scientific, rigorous, and reproducible direction. This breakthrough provides a new paradigm for future AI research and is expected to drive the entire industry toward a more transparent and open development path.
