A single achievement makes one famous worldwide! This time, not even Liang Wenfeng anticipated it, nor did the entire nation 一舉成名天下知！今次不僅梁文鋒沒想到， 就是全國人民也沒想到……

Following in the footsteps of pioneering greats such as Li Siguang, Qian Xuesen, and Tu Youyou, Liang Wenfeng has graced the cover of Nature magazine. The DeepSeek-R1 reasoning model was evaluated as “superior to existing mainstream large models in terms of performance, interpretability, and computational efficiency.”

Since its open-source release, this model has been downloaded over 10.9 million times, significantly advancing the entire field! It may even have the potential to reshape the entire AI landscape!

On September 18, 2025, when the latest issue of Nature revealed its cover, the entire AI community was abuzz. The cover article, DeepSeek-R1: Igniting Large Model Reasoning Capability Through Reinforcement Learning, detailed the breakthrough progress of this original Chinese large model. The name of Liang Wenfeng’s team, the corresponding authors, became inextricably linked with “rewriting the paradigm of AI reasoning.”

Who could have imagined that this “star model,” which now holds the record of 10.9 million downloads on the Hugging Face platform, was actually born from a “risky experiment” that omitted traditional training steps? The team boldly skipped the supervised fine-tuning stage, which typically relies on human examples, and instead used a pure reinforcement learning framework to let the model evolve autonomously—simply instructing it to “write the thought process within tags” and providing rewards based on the correctness of the final answer, then letting the AI “grow wild” on its own.

The curves on the monitoring screen don’t lie: in tests based on the AIME math competition, the model’s problem-solving accuracy soared from an initial 15.6% to 77.9%, and with self-consistent decoding techniques, it reached 86.7%, far surpassing the average level of human participants.

Even more astonishing was the miraculous “Eureka moment” during training. When the model suddenly started frequently using the word “wait,” the developers realized that the AI had learned to pause and reflect on its own problem-solving steps. The emergence of this advanced reasoning ability precisely demonstrated the success of the team’s training philosophy of “not teaching methods, only providing goals.”

Underpinning all this were tangible technological breakthroughs and extreme cost control. The team’s adopted GRPO algorithm was more efficient than traditional methods, and when combined with a dynamic gating mechanism that precisely allocated computational resources, the model achieved a leap in performance with a training cost of only $294,000—less than one-tenth the cost of comparable models.

A Morgan Stanley report stated plainly that DeepSeek-R1 proves “bigger doesn’t mean smarter.” By optimizing data quality and architectural design, the Chinese team has blazed a new trail in AI development characterized by low cost and high efficiency. Moreover, the open-source strategy maximizes the technology’s value: developers worldwide have created over 500 derivative models based on it, and this “Chinese brain” can be seen everywhere, from financial risk control to industrial IoT.

The “full-blooded version” of the model deployed by Wuhan Cloud based on Ascend chips is already providing secure and efficient intelligent services for government users. A security version developed through a collaboration between Zhejiang University and Huawei achieved nearly 100% success rates in defending against harmful content across 14 dimensions.

From a bold experiment in the lab to the cover of an international top-tier journal, DeepSeek-R1’s journey to success is full of surprises. As AI begins to think autonomously and Chinese technology becomes a core force in the global open-source ecosystem, we may be witnessing the dawn of a new intelligent era!

繼李四光、錢學森、屠呦呦等先賢偉人之後，梁文鋒榮登《Nature》雜誌封面。 DeepSeek-R1推理模型，被評審認為“在性能、可解釋性和計算效率方面優於現有主流大模型”。

這個開源模型一經開源就下載了超過1090萬次，極大地推動了整個領域的發展！ 甚至有可能改變整個AI的格局！

2025年9月18日，當《Nature》雜誌最新一期封面揭曉時，整個AI圈都沸騰了。封面文章《DeepSeek-R1:通過強化學習激發大模型推理能力》詳細披露了這款中國原創大模型的突破性進展，而通訊作者梁文鋒團隊的名字，從此與“改寫AI推理範式”緊密相連。

誰能想到，這個如今在Hugging Face平台創下1090萬次下載紀錄的“明星模型”，最初竟是在省略傳統訓練步驟的“冒險實驗”中誕生的。團隊大膽跳過了依賴人類示例的監督微調階段，轉而用純粹的強化學習框架讓模型自主進化——只告訴它“思考過程要寫在< think>標籤里”，再根據最終答案是否正確給予獎勵，剩下的就讓AI自己“野蠻生長”。

監控屏上的曲線不會說謊：在AIME數學競賽測試中，模型的解題準確率從最初的15.6%一路飆升到77.9%，配合自洽解碼技術更是達到86.7%，遠超人類選手平均水平。

更令人驚嘆的是訓練過程中那個神奇的“頓悟時刻”，當模型突然高頻使用“wait”這個詞時，研發人員意識到，AI已經學會了停下來反思自己的解題步驟，這種高級推理能力的湧現，恰恰印證了團隊“不教方法只給目標”的訓練哲學多麼成功。

支撐這一切的，是實實在在的技術突破和極致的成本控制。團隊採用的GRPO算法比傳統方法更高效，配合動態門控機制精準調度計算資源，讓模型在僅29.4萬美元的訓練成本下實現了跨越式提升——這個數字還不到同類模型的十分之一。

摩根士丹利的報告直言，DeepSeek-R1證明了“更大不等於更聰明”，通過優化數據質量和架構設計，中國團隊走出了一條低成本高效能的AI發展新路。而開源策略更讓技術價值最大化，全球開發者基於它創建了500多個衍生模型，從金融風控到工業物聯網，到處都能看到這個“中國大腦”的身影。

武漢雲基於昇騰芯片部署的“滿血版”模型，已經能為政務用戶提供安全高效的智能服務；浙江大學與華為合作開發的安全版本，更是在14個維度的有害內容防禦中達到近100 %的成功率 。

從實驗室里的大膽嘗試，到登上國際頂刊封面，DeepSeek-R1的逆襲之路藏着太多驚喜。當AI開始自主思考，當中國技術成為全球開源生態的核心力量，我們或許正在見證一個全新智能時代的到來！