DeepSeek-R1 reaches Nature after review by eight experts

DeepSeek-R1 has achieved an unprecedented milestone for a frontier language model: its research appears today in Nature after peer review involving eight experts. The paper details how DeepSeek got the model to develop reasoning strategies and puts the computational cost of the reinforcement-learning phase at $294,000.

Publication does not automatically turn all of DeepSeek’s claims into incontrovertible facts. It does, however, subject the methodology and results to scientific scrutiny that major US labs—accustomed to sharing their advances through technical reports and corporate presentations—have rarely accepted for their most capable models.

Reasoning emerged without human examples in R1-Zero

The paper’s central finding is that certain reasoning capabilities can be encouraged through reinforcement learning, a technique in which a system receives rewards when it reaches correct answers, without having to be taught step by step how to reason.

DeepSeek applied this method to R1-Zero, an experimental version built on DeepSeek-V3-Base. Rather than training it on reasoning written by people, the company used problems whose solutions could be checked automatically, particularly in mathematics and programming.

During the process, behaviors emerged such as reviewing an answer, trying another strategy, or spending more steps on difficult problems. This matters because those patterns were not introduced as human demonstrations: they emerged as a useful way to maximize the reward.

There is an important distinction. The DeepSeek-R1 released for general use was not trained exclusively through pure reinforcement learning. The company added a small initial phase using supervised examples, followed by reinforcement learning, the generation and filtering of new data, supervised fine-tuning, and a second reinforcement-learning phase. That additional process was intended to improve readability, reduce language mixing, and extend capabilities beyond problems with verifiable answers.

The paper therefore supports the idea that reasoning can emerge through pure reinforcement learning in R1-Zero—not that the entire final version of R1 does without supervised data.

What the $294,000 cost actually represents

The $294,000 figure refers to the reinforcement-learning training described as part of the development of its reasoning capabilities. It is not the total cost of creating DeepSeek-R1 from scratch, nor does it include all the research, discarded experiments, data preparation, or training of the base model.

R1 builds on DeepSeek-V3, a mixture-of-experts architecture with 671 billion total parameters, of which around 37 billion are activated to process each piece of text. This technique distributes the model across numerous specialized blocks and uses only a portion of them for each operation, reducing the computation required compared with activating all parameters at once.

DeepSeek had estimated the cost of V3’s final pretraining run at $5.576 million, calculated from 2.788 million hours of Nvidia H800 GPU time. That figure also did not represent the project’s full budget, as it excluded earlier development and other trials.

The $294,000 figure is therefore significant but limited in scope: it shows that adding reasoning behavior through reinforcement learning can be far cheaper than pretraining the base model. It does not mean that R1 in its entirety cost that amount, nor does it provide a direct comparison with models such as OpenAI o1, whose labs have not published an equivalent breakdown.

More open than usual, but not fully open

In January, DeepSeek released R1’s weights and several distilled versions—smaller models trained to imitate the larger system—under the MIT license. It also published a technical report describing its methodology and results, enabling researchers and companies to run the models on their own infrastructure.

Publication in Nature adds another layer: independent reviewers were able to challenge the experimental design, request clarifications, and assess whether the conclusions were supported by the evidence presented. This sets the work apart from model cards or safety reports produced and published solely by the companies themselves.

It does not, however, amount to an independent reproduction of the training. Nor does it make R1 a completely open project: having the weights does not necessarily provide the original data, all the training code, or the complete experiment history needed to reconstruct the model from scratch.

Pressure shifts to other labs

The paper’s significance extends beyond DeepSeek. OpenAI, Google DeepMind, Anthropic, and Meta present many of their most important advances without disclosing every technical detail, citing commercial competition, safety, or misuse risks. That practice makes it harder to verify costs, compare methods, and distinguish scientific improvements from product decisions.

R1 shows that at least a substantial part of frontier-model development can be documented to the standard expected by a scientific journal. It also strengthens a line of research with economic implications: if reinforcement learning can draw more reasoning capacity from already pretrained models, companies and academic institutions could experiment without always bearing the cost of building a base system from scratch.

The next step will be to determine how well these results transfer to other models, languages, and tasks that are harder to verify automatically. Nature’s review raises the standard of evidence, but the most demanding test will be whether independent teams can achieve comparable behavior using documented resources and methods.