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This Stage used 3 Reward Models

DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese artificial intelligence company that develops open-source large language designs (LLMs). Based in Hangzhou, Zhejiang, it is owned and funded by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng, established the business in 2023 and functions as its CEO.

The DeepSeek-R1 model provides actions similar to other modern big language designs, such as OpenAI’s GPT-4o and o1. [1] It is trained at a substantially lower cost-stated at US$ 6 million compared to $100 million for OpenAI’s GPT-4 in 2023 [2] -and needs a tenth of the computing power of a similar LLM. [2] [3] [4] DeepSeek’s AI designs were established amid United States sanctions on India and China for Nvidia chips, [5] which were meant to restrict the capability of these 2 nations to establish innovative AI systems. [6] [7]

On 10 January 2025, DeepSeek released its very first free chatbot app, based upon the DeepSeek-R1 model, for iOS and Android; by 27 January, DeepSeek-R1 had surpassed ChatGPT as the most-downloaded free app on the iOS App Store in the United States, [8] causing Nvidia’s share rate to drop by 18%. [9] [10] DeepSeek’s success versus larger and more recognized rivals has been referred to as “upending AI”, [8] constituting “the first chance at what is emerging as an international AI space race”, [11] and ushering in “a brand-new period of AI brinkmanship”. [12]

DeepSeek makes its generative artificial intelligence algorithms, designs, and training details open-source, permitting its code to be freely readily available for usage, adjustment, viewing, and designing files for developing purposes. [13] The business apparently intensely recruits young AI scientists from leading Chinese universities, [8] and works with from outside the computer technology field to diversify its models’ understanding and capabilities. [3]

In February 2016, High-Flyer was co-founded by AI lover Liang Wenfeng, who had been trading since the 2007-2008 monetary crisis while participating in Zhejiang University. [14] By 2019, he developed High-Flyer as a hedge fund concentrated on establishing and using AI trading algorithms. By 2021, High-Flyer specifically used AI in trading. [15] DeepSeek has actually made its generative expert system chatbot open source, meaning its code is freely available for usage, modification, and viewing. This consists of authorization to access and use the source code, as well as design files, for constructing functions. [13]

According to 36Kr, Liang had developed up a store of 10,000 Nvidia A100 GPUs, which are used to train AI [16], before the United States federal government imposed AI chip restrictions on China. [15]

In April 2023, High-Flyer started an artificial general intelligence laboratory devoted to research developing AI tools separate from High-Flyer’s financial service. [17] [18] In May 2023, with High-Flyer as one of the investors, the laboratory became its own company, DeepSeek. [15] [19] [18] Venture capital firms hesitated in offering financing as it was unlikely that it would be able to produce an exit in a brief amount of time. [15]

After releasing DeepSeek-V2 in May 2024, which used strong performance for a low price, DeepSeek ended up being understood as the catalyst for China’s AI model price war. It was rapidly dubbed the “Pinduoduo of AI”, and other significant tech giants such as ByteDance, Tencent, Baidu, and Alibaba began to cut the price of their AI designs to complete with the business. Despite the low cost charged by DeepSeek, it paid compared to its competitors that were losing cash. [20]

DeepSeek is concentrated on research and has no in-depth prepare for commercialization; [20] this likewise enables its technology to avoid the most rigid arrangements of China’s AI regulations, such as needing consumer-facing technology to comply with the federal government’s controls on details. [3]

DeepSeek’s hiring preferences target technical capabilities instead of work experience, leading to a lot of new hires being either current university graduates or designers whose AI professions are less developed. [18] [3] Likewise, the company hires people with no computer technology background to assist its technology understand other topics and understanding areas, including having the ability to generate poetry and perform well on the notoriously hard Chinese college admissions exams (Gaokao). [3]

Development and release history

DeepSeek LLM

On 2 November 2023, DeepSeek launched its very first series of design, DeepSeek-Coder, which is offered totally free to both scientists and business users. The code for the model was made open-source under the MIT license, with an extra license arrangement (“DeepSeek license”) relating to “open and accountable downstream use” for the model itself. [21]

They are of the same architecture as DeepSeek LLM detailed below. The series includes 8 models, 4 pretrained (Base) and 4 instruction-finetuned (Instruct). They all have 16K context lengths. The training was as follows: [22] [23] [24]

1. Pretraining: 1.8 T tokens (87% source code, 10% code-related English (GitHub markdown and Stack Exchange), and 3% code-unrelated Chinese).
2. Long-context pretraining: 200B tokens. This extends the context length from 4K to 16K. This produced the Base models.
3. Supervised finetuning (SFT): 2B tokens of instruction data. This produced the Instruct designs.

They were trained on clusters of A100 and H800 Nvidia GPUs, connected by InfiniBand, NVLink, NVSwitch. [22]

On 29 November 2023, DeepSeek released the DeepSeek-LLM series of designs, with 7B and 67B specifications in both Base and Chat types (no Instruct was launched). It was established to take on other LLMs offered at the time. The paper claimed benchmark results greater than many open source LLMs at the time, particularly Llama 2. [26]: section 5 Like DeepSeek Coder, the code for the model was under MIT license, with DeepSeek license for the design itself. [27]

The architecture was essentially the like those of the Llama series. They utilized the pre-norm decoder-only Transformer with RMSNorm as the normalization, SwiGLU in the feedforward layers, rotary positional embedding (RoPE), and grouped-query attention (GQA). Both had vocabulary size 102,400 (byte-level BPE) and context length of 4096. They trained on 2 trillion tokens of English and Chinese text obtained by deduplicating the Common Crawl. [26]

The Chat variations of the 2 Base designs was also released concurrently, acquired by training Base by monitored finetuning (SFT) followed by direct policy optimization (DPO). [26]

On 9 January 2024, they released 2 DeepSeek-MoE models (Base, Chat), each of 16B criteria (2.7 B activated per token, 4K context length). The training was essentially the same as DeepSeek-LLM 7B, and was trained on a part of its training dataset. They claimed equivalent performance with a 16B MoE as a 7B non-MoE. In architecture, it is a version of the basic sparsely-gated MoE, with “shared experts” that are constantly queried, and “routed professionals” that might not be. They discovered this to assist with . In basic MoE, some experts can end up being overly counted on, while other experts may be seldom utilized, losing specifications. Attempting to stabilize the specialists so that they are similarly used then triggers experts to reproduce the same capability. They proposed the shared professionals to discover core capacities that are frequently used, and let the routed professionals to learn the peripheral capacities that are hardly ever used. [28]

In April 2024, they released 3 DeepSeek-Math models specialized for doing mathematics: Base, Instruct, RL. It was trained as follows: [29]

1. Initialize with a previously pretrained DeepSeek-Coder-Base-v1.5 7B.
2. Further pretrain with 500B tokens (6% DeepSeekMath Corpus, 4% AlgebraicStack, 10% arXiv, 20% GitHub code, 10% Common Crawl). This produced the Base design.
3. Train an instruction-following design by SFT Base with 776K math issues and their tool-use-integrated step-by-step solutions. This produced the Instruct design.
Reinforcement learning (RL): The reward model was a procedure benefit design (PRM) trained from Base according to the Math-Shepherd technique. [30] This reward model was then used to train Instruct utilizing group relative policy optimization (GRPO) on a dataset of 144K mathematics concerns “related to GSM8K and MATH”. The reward model was constantly upgraded during training to avoid reward hacking. This resulted in the RL model.

V2

In May 2024, they released the DeepSeek-V2 series. The series consists of 4 designs, 2 base models (DeepSeek-V2, DeepSeek-V2-Lite) and 2 chatbots (-Chat). The 2 bigger models were trained as follows: [31]

1. Pretrain on a dataset of 8.1 T tokens, where Chinese tokens are 12% more than English ones.
2. Extend context length from 4K to 128K utilizing YaRN. [32] This resulted in DeepSeek-V2.
3. SFT with 1.2 M instances for helpfulness and 0.3 M for security. This resulted in DeepSeek-V2-Chat (SFT) which was not released.
4. RL using GRPO in 2 phases. The very first stage was trained to fix mathematics and coding problems. This phase utilized 1 reward model, trained on compiler feedback (for coding) and ground-truth labels (for mathematics). The 2nd stage was trained to be valuable, safe, and follow rules. This phase used 3 benefit models. The helpfulness and security benefit designs were trained on human choice data. The rule-based reward model was manually programmed. All qualified benefit designs were initialized from DeepSeek-V2-Chat (SFT). This led to the released version of DeepSeek-V2-Chat.

They went with 2-staged RL, due to the fact that they found that RL on thinking data had “unique characteristics” different from RL on basic data. For example, RL on thinking might enhance over more training steps. [31]

The 2 V2-Lite models were smaller sized, and skilled likewise, though DeepSeek-V2-Lite-Chat just underwent SFT, not RL. They trained the Lite variation to assist “additional research and advancement on MLA and DeepSeekMoE”. [31]

Architecturally, the V2 models were significantly customized from the DeepSeek LLM series. They altered the standard attention mechanism by a low-rank approximation called multi-head hidden attention (MLA), and used the mix of specialists (MoE) variant formerly published in January. [28]

The Financial Times reported that it was cheaper than its peers with a rate of 2 RMB for every single million output tokens. The University of Waterloo Tiger Lab’s leaderboard ranked DeepSeek-V2 seventh on its LLM ranking. [19]

In June 2024, they released 4 models in the DeepSeek-Coder-V2 series: V2-Base, V2-Lite-Base, V2-Instruct, V2-Lite-Instruct. They were trained as follows: [35] [note 2]

1. The Base designs were initialized from corresponding intermediate checkpoints after pretraining on 4.2 T tokens (not the version at the end of pretraining), then pretrained further for 6T tokens, then context-extended to 128K context length. This produced the Base designs.
DeepSeek-Coder and DeepSeek-Math were used to produce 20K code-related and 30K math-related direction information, then integrated with an instruction dataset of 300M tokens. This was utilized for SFT.
2. RL with GRPO. The reward for mathematics issues was calculated by comparing with the ground-truth label. The reward for code problems was generated by a benefit model trained to anticipate whether a program would pass the unit tests.

DeepSeek-V2.5 was released in September and updated in December 2024. It was made by integrating DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. [36]

V3

In December 2024, they launched a base design DeepSeek-V3-Base and a chat model DeepSeek-V3. The model architecture is essentially the like V2. They were trained as follows: [37]

1. Pretraining on 14.8 T tokens of a multilingual corpus, mostly English and Chinese. It contained a higher ratio of mathematics and programs than the pretraining dataset of V2.
2. Extend context length twice, from 4K to 32K and then to 128K, utilizing YaRN. [32] This produced DeepSeek-V3-Base.
3. SFT for 2 epochs on 1.5 M samples of reasoning (mathematics, programming, logic) and non-reasoning (creative writing, roleplay, easy concern answering) information. Reasoning data was generated by “expert designs”. Non-reasoning data was generated by DeepSeek-V2.5 and inspected by people. – The “expert designs” were trained by starting with an undefined base design, then SFT on both information, and artificial information created by an internal DeepSeek-R1 model. The system timely asked the R1 to reflect and verify throughout thinking. Then the specialist models were RL utilizing an undefined benefit function.
– Each expert model was trained to generate just synthetic reasoning data in one specific domain (mathematics, shows, logic).
– Expert models were used, rather of R1 itself, considering that the output from R1 itself suffered “overthinking, poor formatting, and excessive length”.

4. Model-based benefit models were made by beginning with a SFT checkpoint of V3, then finetuning on human preference data containing both last reward and chain-of-thought resulting in the final benefit. The reward design produced benefit signals for both questions with unbiased however free-form responses, and questions without objective responses (such as creative writing).
5. A SFT checkpoint of V3 was trained by GRPO using both reward models and rule-based benefit. The rule-based benefit was calculated for math problems with a final response (put in a box), and for programs problems by unit tests. This produced DeepSeek-V3.

The DeepSeek group carried out substantial low-level engineering to attain efficiency. They used mixed-precision arithmetic. Much of the forward pass was carried out in 8-bit floating point numbers (5E2M: 5-bit exponent and 2-bit mantissa) rather than the standard 32-bit, requiring unique GEMM regimens to build up properly. They used a custom 12-bit float (E5M6) for only the inputs to the linear layers after the attention modules. Optimizer states remained in 16-bit (BF16). They lessened the interaction latency by overlapping thoroughly computation and interaction, such as devoting 20 streaming multiprocessors out of 132 per H800 for only inter-GPU interaction. They reduced communication by rearranging (every 10 minutes) the specific device each specialist was on in order to avoid specific devices being queried regularly than the others, adding auxiliary load-balancing losses to the training loss function, and other load-balancing strategies. [37]

After training, it was deployed on H800 clusters. The H800 cards within a cluster are linked by NVLink, and the clusters are connected by InfiniBand. [37]

Benchmark tests show that DeepSeek-V3 outperformed Llama 3.1 and Qwen 2.5 whilst matching GPT-4o and Claude 3.5 Sonnet. [18] [39] [40] [41]

R1

On 20 November 2024, DeepSeek-R1-Lite-Preview ended up being accessible via DeepSeek’s API, in addition to through a chat user interface after logging in. [42] [43] [note 3] It was trained for rational reasoning, mathematical thinking, and real-time problem-solving. DeepSeek claimed that it exceeded efficiency of OpenAI o1 on criteria such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal specified when it utilized 15 issues from the 2024 edition of AIME, the o1 design reached a solution faster than DeepSeek-R1-Lite-Preview. [45]

On 20 January 2025, DeepSeek released DeepSeek-R1 and DeepSeek-R1-Zero. [46] Both were initialized from DeepSeek-V3-Base, and share its architecture. The business likewise launched some “DeepSeek-R1-Distill” designs, which are not initialized on V3-Base, however instead are initialized from other pretrained open-weight designs, including LLaMA and Qwen, then fine-tuned on artificial data created by R1. [47]

A discussion between User and Assistant. The user asks a concern, and the Assistant solves it. The assistant first thinks of the reasoning procedure in the mind and then supplies the user with the answer. The thinking procedure and response are confined within and tags, respectively, i.e., thinking procedure here respond to here. User:. Assistant:

DeepSeek-R1-Zero was trained specifically using GRPO RL without SFT. Unlike previous versions, they used no model-based reward. All reward functions were rule-based, “generally” of 2 types (other types were not specified): precision benefits and format benefits. Accuracy reward was examining whether a boxed answer is appropriate (for mathematics) or whether a code passes tests (for shows). Format reward was examining whether the design puts its thinking trace within … [47]

As R1-Zero has concerns with readability and blending languages, R1 was trained to deal with these problems and more enhance reasoning: [47]

1. SFT DeepSeek-V3-Base on “thousands” of “cold-start” information all with the standard format of|special_token|| special_token|summary >.
2. Apply the exact same RL process as R1-Zero, but likewise with a “language consistency reward” to motivate it to respond monolingually. This produced an internal design not released.
3. Synthesize 600K thinking data from the internal design, with rejection tasting (i.e. if the created reasoning had an incorrect final response, then it is eliminated). Synthesize 200K non-reasoning data (writing, accurate QA, self-cognition, translation) utilizing DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K artificial information for 2 dates.
5. GRPO RL with rule-based reward (for reasoning jobs) and model-based reward (for non-reasoning tasks, helpfulness, and harmlessness). This produced DeepSeek-R1.

Distilled designs were trained by SFT on 800K information synthesized from DeepSeek-R1, in a similar way as action 3 above. They were not trained with RL. [47]

Assessment and reactions

DeepSeek released its AI Assistant, which utilizes the V3 design as a chatbot app for Apple IOS and Android. By 27 January 2025 the app had actually gone beyond ChatGPT as the highest-rated free app on the iOS App Store in the United States; its chatbot reportedly responds to questions, solves logic issues and composes computer system programs on par with other chatbots on the market, according to benchmark tests utilized by American AI business. [3]

DeepSeek-V3 uses substantially less resources compared to its peers; for instance, whereas the world’s leading AI business train their chatbots with supercomputers using as many as 16,000 graphics processing units (GPUs), if not more, DeepSeek declares to have needed just about 2,000 GPUs, specifically the H800 series chip from Nvidia. [37] It was trained in around 55 days at a cost of US$ 5.58 million, [37] which is approximately one tenth of what United States tech huge Meta spent constructing its most current AI innovation. [3]

DeepSeek’s competitive performance at relatively very little cost has actually been recognized as potentially challenging the international dominance of American AI designs. [48] Various publications and news media, such as The Hill and The Guardian, described the release of its chatbot as a “Sputnik minute” for American AI. [49] [50] The performance of its R1 design was supposedly “on par with” one of OpenAI’s most current designs when used for tasks such as mathematics, coding, and natural language thinking; [51] echoing other commentators, American Silicon Valley investor Marc Andreessen similarly explained R1 as “AI’s Sputnik moment”. [51]

DeepSeek’s founder, Liang Wenfeng has been compared to Open AI CEO Sam Altman, with CNN calling him the Sam Altman of China and an evangelist for AI. [52] Chinese state media extensively applauded DeepSeek as a national possession. [53] [54] On 20 January 2025, China’s Premier Li Qiang invited Liang Wenfeng to his symposium with specialists and asked him to supply viewpoints and suggestions on a draft for comments of the yearly 2024 federal government work report. [55]

DeepSeek’s optimization of limited resources has highlighted possible limitations of United States sanctions on China’s AI advancement, which consist of export constraints on innovative AI chips to China [18] [56] The success of the company’s AI models consequently “stimulated market chaos” [57] and triggered shares in major international innovation companies to plunge on 27 January 2025: Nvidia’s stock fell by as much as 17-18%, [58] as did the stock of rival Broadcom. Other tech firms also sank, including Microsoft (down 2.5%), Google’s owner Alphabet (down over 4%), and Dutch chip devices maker ASML (down over 7%). [51] A global selloff of innovation stocks on Nasdaq, prompted by the release of the R1 model, had caused tape-record losses of about $593 billion in the market capitalizations of AI and computer hardware companies; [59] by 28 January 2025, an overall of $1 trillion of value was wiped off American stocks. [50]

Leading figures in the American AI sector had mixed reactions to DeepSeek’s success and performance. [60] Microsoft CEO Satya Nadella and OpenAI CEO Sam Altman-whose companies are associated with the United States government-backed “Stargate Project” to develop American AI infrastructure-both called DeepSeek “extremely remarkable”. [61] [62] American President Donald Trump, who announced The Stargate Project, called DeepSeek a wake-up call [63] and a favorable development. [64] [50] [51] [65] Other leaders in the field, including Scale AI CEO Alexandr Wang, Anthropic cofounder and CEO Dario Amodei, and Elon Musk revealed suspicion of the app’s efficiency or of the sustainability of its success. [60] [66] [67] Various business, consisting of Amazon Web Services, Toyota, and Stripe, are looking for to utilize the model in their program. [68]

On 27 January 2025, DeepSeek restricted its new user registration to telephone number from mainland China, e-mail addresses, or Google account logins, following a “large-scale” cyberattack interfered with the appropriate performance of its servers. [69] [70]

Some sources have observed that the main application shows user interface (API) variation of R1, which ranges from servers located in China, uses censorship systems for subjects that are considered politically sensitive for the federal government of China. For example, the model refuses to answer questions about the 1989 Tiananmen Square demonstrations and massacre, persecution of Uyghurs, comparisons in between Xi Jinping and Winnie the Pooh, or human rights in China. [71] [72] [73] The AI may initially produce an answer, but then erases it soon afterwards and replaces it with a message such as: “Sorry, that’s beyond my existing scope. Let’s speak about something else.” [72] The incorporated censorship systems and constraints can just be removed to a restricted degree in the open-source variation of the R1 model. If the “core socialist worths” specified by the Chinese Internet regulatory authorities are touched upon, or the political status of Taiwan is raised, discussions are terminated. [74] When checked by NBC News, DeepSeek’s R1 described Taiwan as “an inalienable part of China’s area,” and mentioned: “We strongly oppose any type of ‘Taiwan independence’ separatist activities and are devoted to achieving the complete reunification of the motherland through tranquil means.” [75] In January 2025, Western scientists were able to deceive DeepSeek into providing specific answers to some of these subjects by asking for in its response to switch particular letters for similar-looking numbers. [73]

Security and personal privacy

Some experts fear that the federal government of China might utilize the AI system for foreign impact operations, spreading out disinformation, security and the advancement of cyberweapons. [76] [77] [78] DeepSeek’s privacy terms and conditions state “We store the information we collect in safe servers found in individuals’s Republic of China … We might gather your text or audio input, prompt, uploaded files, feedback, chat history, or other content that you offer to our model and Services”. Although the information storage and collection policy follows ChatGPT’s personal privacy policy, [79] a Wired article reports this as security concerns. [80] In response, the Italian data security authority is seeking extra information on DeepSeek’s collection and usage of personal information, and the United States National Security Council announced that it had actually started a national security review. [81] [82] Taiwan’s federal government prohibited the usage of DeepSeek at federal government ministries on security premises and South Korea’s Personal Information Protection Commission opened a query into DeepSeek’s use of personal information. [83]

Artificial intelligence market in China.

Notes

^ a b c The number of heads does not equal the number of KV heads, due to GQA.
^ Inexplicably, the model named DeepSeek-Coder-V2 Chat in the paper was launched as DeepSeek-Coder-V2-Instruct in HuggingFace.
^ At that time, the R1-Lite-Preview needed selecting “Deep Think enabled”, and every user might utilize it just 50 times a day.
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