DeepSeek-Prover-V2 Model Aimed At Formal Mathematical Reasoning

DeepSeek has introduced DeepSeek-Prover-V2, an open-source large language model tailored for formal theorem proving in Lean 4. The model gathers initialisation data using a recursive theorem proving pipeline powered by DeepSeek-V3. The model employs a cold-start training method that involves decomposing complex problems into subgoals using DeepSeek-V3, synthesising proofs, and enhancing performance through reinforcement learning.

Achieving an 88.9% pass rate on the MiniF2F-test and solving 49 out of 658 PutnamBench problems, DeepSeek-Prover-V2 demonstrates advanced capabilities in neural theorem proving. The release includes ProverBench, a dataset comprising 325 formalized mathematical problems drawn from AIME competitions and educational materials. Available in two sizes—7B and 671B parameters—the model is accessible on Hugging Face for further research and application.

Introduction to DeepSeek-Prover-V2

DeepSeek-Prover-V2 is an open-source large language model specifically designed for formal theorem proving in Lean 4, a dependently typed programming language. The model leverages a recursive theorem-proving pipeline powered by DeepSeek-V3, enabling it to decompose complex mathematical problems into manageable subgoals. This approach facilitates the creation of synthetic proof data, which is then used to refine the model’s performance through reinforcement learning.

The architecture of DeepSeek-Prover-V2 is built to handle formal verification tasks efficiently, making it a valuable tool for researchers and practitioners in automated theorem proving. Available on Hugging Face in two versions—7B and 67B parameters—the model caters to different computational needs while maintaining its core functionality in formal theorem proving.

DeepSeek-Prover-V2’s applications span across various domains, including automated theorem proving, formal methods, and educational tools for mathematics and logic. Its design emphasizes precision and scalability, positioning it as a robust solution for advancing research and practical applications in formal theorem proving.

Model Summary and Training Process

DeepSeek-Prover-V2 utilizes synthetic proof data generation to enhance its capabilities in mathematical reasoning and problem-solving. The model is trained using reinforcement learning, where proofs are evaluated based on correctness, completeness, and adherence to mathematical rigor. This iterative process allows the model to continuously improve its performance across a wide range of problems.

The ProverBench dataset plays a critical role in evaluating DeepSeek-Prover-V2’s effectiveness. Comprising a diverse set of problems and their corresponding proofs, this dataset serves as a benchmark for assessing the model’s ability to handle complex mathematical challenges.

The ProverBench dataset is designed to evaluate DeepSeek-Prover-V2’s performance across various mathematical domains. It includes a wide range of problems, from foundational concepts to advanced topics, ensuring comprehensive coverage of potential challenges. This dataset not only tests the model’s accuracy but also its ability to generate clear and concise proofs.

DeepSeek-Prover-V2 is available for download in two versions: 7B and 67B parameters, catering to different computational requirements. Researchers and developers can access these models on Hugging Face, along with associated documentation and resources. The ProverBench dataset is also publicly accessible, enabling further research and benchmarking.

DeepSeek-Prover-V2 stands as a powerful tool for advancing research in formal theorem proving and mathematical reasoning, offering both precision and scalability for diverse applications.