Seek .(US:SKLTY) 3 6 Ke·2025-09-18 02:02Core Insights - The value of open-source artificial intelligence (AI) is gaining broader recognition, highlighted by the publication of the DeepSeek-R1 paper in the prestigious journal Nature, with founder Liang Wenfeng as the corresponding author [1][5]. Research Findings - The research team hypothesized that human-defined reasoning patterns might limit model exploration, and unrestricted reinforcement learning (RL) training could better stimulate the emergence of new reasoning capabilities in large language models (LLMs) [3][8]. - Experiments demonstrated that the reasoning ability of LLMs can be enhanced through pure RL, reducing the need for human input, and outperforming traditionally trained LLMs in tasks such as mathematics, programming competitions, and graduate-level STEM problems [3][9]. Model Evaluation - Following the launch of DeepSeek-R1, it received widespread acclaim from global developers, achieving 91.1k stars on GitHub [4]. - Nature's editorial recognized DeepSeek-R1 as the first mainstream LLM published after peer review, marking a significant step towards transparency in AI [5][17]. - The editorial emphasized the importance of peer-reviewed publications in clarifying LLM operations and assessing their authenticity [6][17]. Methodology - The research introduced a new paradigm within the RL framework, minimizing reliance on human-annotated reasoning processes and exploring the potential for LLMs to develop reasoning capabilities through self-evolution [9][10]. - The team proposed a RL algorithm called "Group Relative Policy Optimization" (GRPO) and trained various models, including DeepSeek-R1-Zero and DeepSeek-R1, based on the foundational model DeepSeek-V3 Base [10][12]. Training Phases - The training process involved multiple stages, with each subsequent model improving upon the previous one in terms of reasoning and instruction-following capabilities [14]. - DeepSeek-R1 demonstrated strong reasoning abilities aligned with human preferences, achieving superior performance across 21 mainstream benchmarks, validating the effectiveness of the RL framework [15][16]. Industry Implications - The editorial raised concerns about the lack of independent peer review for many widely used LLMs, highlighting the need for transparency and accountability in the AI industry [17][18]. - Nature called for more AI companies to submit their models for publication review, emphasizing that peer review can enhance trust and credibility in AI research [18][19].