DeepSeek-Prover-V2 - Support the development of deepseek-ai on GitHub.

DeepSeek-Prover-V2 is an open-source large language model designed for formal theorem proving in Lean 4. It utilizes a recursive theorem proving pipeline powered by DeepSeek-V3 to decompose complex problems into manageable subgoals. This innovative approach integrates informal and formal mathematical reasoning, making it a valuable tool for researchers and developers in the field of mathematics and computer science.

1. Cold-Start Reasoning Data

   The model synthesizes cold-start reasoning data through a recursive proof search, utilizing DeepSeek-V3 for both subgoal decomposition and formalization.

2. Reinforcement Learning

   DeepSeek-Prover-V2 employs reinforcement learning to enhance its ability to bridge informal reasoning with formal proof construction, using binary feedback for reward supervision.

3. ProverBench Dataset

   It includes a benchmark dataset, ProverBench, with 325 problems from high-school competitions and textbook examples, allowing for comprehensive evaluation across various mathematical domains.

4. Model Variants

   Available in two sizes, 7B and 671B parameters, DeepSeek-Prover-V2 caters to different computational needs and contexts.

5. Integration with Hugging Face

   Users can easily utilize the models through Hugging Face's Transformers for model inference, facilitating quick deployment and experimentation.

6. High Performance

   The model achieves state-of-the-art performance in neural theorem proving, with an impressive pass ratio on benchmark tests.

1. Visit the Hugging Face website to access the DeepSeek-Prover-V2 models.
2. Use the provided code snippets to quickly set up the model for inference.
3. Familiarize yourself with the ProverBench dataset to understand the types of problems the model can solve.
4. Experiment with both model sizes to find the best fit for your computational resources.

What is DeepSeek-Prover-V2?

DeepSeek-Prover-V2 is an AI-powered model designed for formal theorem proving in Lean 4, leveraging advanced techniques in reinforcement learning and recursive proof search.

How does DeepSeek-Prover-V2 work?

The model decomposes complex theorems into subgoals, synthesizes proofs, and utilizes reinforcement learning to improve its reasoning capabilities.

Can I use DeepSeek-Prover-V2 for my projects?

Yes, the model is open-source and can be integrated into various projects involving formal reasoning and theorem proving.

How is DeepSeek-Prover-V2 different from other theorem provers?

It combines informal reasoning with formal proof construction through a unique cold-start training process and reinforcement learning, achieving high performance in theorem proving tasks.

How do I get started with DeepSeek-Prover-V2?

You can start by visiting the Hugging Face page for DeepSeek-Prover-V2, where you can find documentation and examples for using the model.

Price data is not available yet; please check the official GitHub repository for updates.

1. DeepSeek-Prover-V2 showcases impressive capabilities in formal theorem proving, effectively integrating informal reasoning with formal proof construction.
2. The model's performance on benchmark datasets indicates its potential for solving complex mathematical problems.
3. However, users may need a solid understanding of formal mathematics and Lean 4 to fully leverage the model's capabilities.
4. The reliance on reinforcement learning may require further tuning and experimentation to optimize performance for specific use cases.
5. Overall, while DeepSeek-Prover-V2 is a powerful tool for researchers and developers, it may have a steep learning curve for those unfamiliar with formal theorem proving.