Deepseek-AI issued Deepseek-Prover-V2: The main model of the big language is designed for legal decay, proving low degeneration and strengthening

Official mathematical thinking is changed into a special subfield of artificial intelligence that requires logical consistent. Unlike problems with problems, allowing comprehension and explicit information, legitimate representations that rely on all-full-defined, accurate, and accurate steps. Assistants, such as declining, COQ, and Isabelle, served as a systematic framework where organized evidence was formed. Their performance requires logical sound outside of the output, approaching, or a minor consideration. This contributes to AI programs, especially large language models, exceeding the production of united environmental language answers but often a stronger equality. However, the desire to combine this power, AI smoothness in the imaginary thought and formation of formal guarantees, lead to new use in the correct language example and legal default.

The main issue arises with the failure of current language models to close the separation of the mind between informal and orderly thinking. Language models usually highlight productions such definitions and solving mathematical problems in nature. However, this consultation is unapported and often consists of formal clarification required by the correct systematic systems. While people can rapely jump in one impairment step in another, witness adversaries need a sequence of steps, free. Therefore, the challenge is to guide AI models to produce integrated integrated results from their informal and accurate process. This problem becomes more complex when handling the developed-in-homes of the number or geometry, where accuracy is important.

Latest attempts have tried to deal with this issue in first-handle models to produce language testimony, which is manually or automatically translated into systematic experiences. A well-known strategy includes decomment with a complex-theorem into limits. Each introduction shows a lemma that can be considered independently and after reoccoming to form a thorough witness. Arties such as “Drafts, Sketch, and prove that” and use this idea, using language models to produce the ideas of evidence that are translated into the official language. One way uses a true Hierarchical reading, breaks the complex musical problems into simple layers. However, these types of models often strive to produce completely reassuring results in soft or coq areas. In addition, these types of training data is usually, and the testimony efforts fail to express effective results that provide practical learning signals.

The research team from Deentieseek-AI has brought the new model, Deepseek-Prever-V2, is designed to produce formal evidence of mathematics. The core of their approach is using Deepseek-V3 to reduce the “'translated” statements in the statement. produced by Deepseek-V3. This creates a cold startup dataset for the reinforcement.

The cold cold pipe begins by quitting Deepseek-v3 to create evidence paintings in the natural language. These drawings are transformed into official -em statements and compound parts. Basic establishment lies with resolution of solving each review using 7b prover, reduces computer cost while maintaining a systematic. Investigators create a curriculum for the severity of training services over time. They also use two types of Theorems below, and the other include the preceding areas such as buildings, and one person behaves independently. This dual building was placed in the IDeration of model's ODeration to train you with the progress of the problem. The power of the model are strengthened by the reward program based on the change during training during training, making sure that all lemmas are properly attached to the final testimony.

At Benchmark-test, the model received an average of 88.9% of the maximum sample. It also resolved 49 of 658 problems from Puntnaminch, a platform with a platform for mathematics. In the newly developed dataset, including the official 325 problems, the model addressed by 6 of 15 problems (American International Mathematics test) Activities 2024 and 2025. These benches highlight the power of formal reasoning at all formal thought. Or compared with Deepseek-v3, using the natural language, the new model shows competing performance, to resolve the amount of AIME's problems while verifying legal capacity.

Several drives based on Deepseek-Prever-V2:

• Deepseek-Prever-V2 has received 88.9% of the passage to Minif2f
• The model was successfully settled 49 of 658 problems from PutNatical Database, consisting of advanced sectors of mathematics.
• It includes 6 of 15 problems from the latest AME 20244-2025 components, indicates real land performance.
• New benchmark, Preverberch, containing 325 legal problems, is presented in testing models in formal consultation models.
• The pipelite proves the drawing of the environment and the construction of organized evidence by combining Deepseek-v3 and Prover-V3 model 7b.
• Two types of low dementia – one and one other than leaning – used to train the model in an orderly manner, directed to curriculum.
• Emphasis on the reward that is designed to sync has improved the accuracy of evidence by enforcing the alignment of a building between skirt and solution.
• The entire Training Strategy depends on the data for the delete startup starters, to eliminate the dependent on handwritten evidence.

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