Seek .(US:SKLTY) 3 6 Ke·2025-09-18 09:56Core Insights - DeepSeek's AI model, R1, has gained significant recognition by being featured on the cover of Nature, a prestigious scientific journal, highlighting its impact in the AI industry [2][10][12] - The training cost for R1 was notably low at $294,000, which contrasts sharply with the multi-million dollar investments typical for models from companies like OpenAI [7][48] - The model's development process involved rigorous peer review, setting a new standard for transparency and scientific validation in AI [11][15][16] Group 1: Model Development and Training - DeepSeek R1's training process was detailed in a paper published on arXiv, which was later expanded upon in the Nature article, showcasing a comprehensive methodology [6][7] - The model was trained using a pure reinforcement learning framework, allowing it to develop reasoning capabilities without relying on human-annotated data [19][41] - R1 achieved an impressive accuracy of 77.9% in the AIME 2024 math competition, surpassing human average scores and even outperforming GPT-4 in certain tasks [23][31] Group 2: Peer Review and Industry Impact - The peer review process for R1 involved independent experts scrutinizing the model, which is a departure from the typical practices of major AI companies that often do not submit their models for academic evaluation [10][11][15] - Nature's editorial team has called for other companies to submit their models for peer review, emphasizing the importance of transparency and accountability in AI development [15][16] - The recognition from Nature not only validates R1's scientific contributions but also positions DeepSeek as a leader in the push for more rigorous standards in AI research [12][50] Group 3: Technical Innovations - R1's architecture is based on a mixture of experts (MoE) model with 671 billion parameters, which was pre-trained on a vast dataset of web pages and e-books [25] - The model's training involved a unique approach where it was rewarded solely based on the correctness of its answers, fostering an environment for self-reflection and dynamic adjustment during problem-solving [29][38] - The final version of R1 was developed through a multi-stage training process that combined reinforcement learning with supervised fine-tuning, enhancing both reasoning and general capabilities [39][47]