DeepSeek Unveils DeepSeek-Prover-V2: Advancing Neural Theorem Proving with Recursive Proof Search and a New Benchmark

Introduction

In this tutorial, you'll learn how to set up and experiment with DeepSeek-Prover-V2, an open-source large language model designed for Lean 4 theorem proving. This cutting-edge system uses recursive proof search and reinforcement learning to achieve state-of-the-art results in neural theorem proving. By following this guide, you'll gain hands-on experience with the tools and techniques used in advanced AI research for automated reasoning.

Prerequisites

Before beginning this tutorial, ensure you have the following:

• Basic understanding of Python programming
• Python 3.8 or higher installed
• Access to a machine with at least 16GB RAM (preferably 32GB or more)
• Git installed for cloning repositories
• Familiarity with Lean 4 theorem proving concepts

Step 1: Environment Setup

1.1 Create a Virtual Environment

First, create a dedicated Python environment to avoid conflicts with other projects:

python -m venv deepseek_env
source deepseek_env/bin/activate  # On Windows: deepseek_env\Scripts\activate

This step isolates our project dependencies and prevents version conflicts with other Python packages.

1.2 Install Required Dependencies

Install the necessary packages for working with Lean and DeepSeek models:

pip install lean4-tools torch transformers datasets

These packages provide the core functionality for theorem proving and model interactions.

Step 2: Clone DeepSeek-Prover-V2 Repository

2.1 Clone the Repository

Clone the DeepSeek-Prover-V2 repository from GitHub:

git clone https://github.com/deepseek-ai/DeepSeek-Prover-V2.git
cd DeepSeek-Prover-V2

This repository contains the implementation, training scripts, and benchmark datasets needed to work with the model.

2.2 Install Local Dependencies

Install the local package dependencies:

pip install -e .

The -e flag installs the package in development mode, allowing you to modify and test code directly.

Step 3: Prepare Training Data

3.1 Download Benchmark Datasets

DeepSeek-Prover-V2 uses the MiniF2F benchmark for evaluation. Download the dataset:

mkdir -p data/minif2f
wget https://github.com/deepseek-ai/DeepSeek-Prover-V2/raw/main/data/minif2f/train.jsonl -O data/minif2f/train.jsonl
wget https://github.com/deepseek-ai/DeepSeek-Prover-V2/raw/main/data/minif2f/val.jsonl -O data/minif2f/val.jsonl

This dataset contains formalized mathematical theorems that the model learns to prove.

3.2 Data Format Understanding

Examine a sample of the training data to understand its structure:

import json
with open('data/minif2f/train.jsonl', 'r') as f:
    sample = json.loads(f.readline())
print(json.dumps(sample, indent=2))

The data includes theorem statements, proof steps, and metadata used for training.

Step 4: Initialize and Train Model

4.1 Configure Training Parameters

Create a configuration file for training:

mkdir -p configs
cat > configs/prover_config.json << EOF
{
  "model_name": "deepseek-ai/DeepSeek-V3",
  "max_length": 2048,
  "batch_size": 4,
  "learning_rate": 5e-5,
  "num_epochs": 3,
  "gradient_accumulation_steps": 8
}
EOF

This configuration sets up the training environment with appropriate parameters for the model.

4.2 Run Training Script

Execute the training process using the provided script:

python train.py --config configs/prover_config.json --output_dir ./models/prover_v2

This command initializes the training process using recursive proof search and reinforcement learning techniques.

Step 5: Evaluate Model Performance

5.1 Run Evaluation Script

After training, evaluate the model on the MiniF2F benchmark:

python evaluate.py --model_path ./models/prover_v2 --data_path data/minif2f/val.jsonl

This script runs the trained model on validation data and reports proof success rates.

5.2 Analyze Results

Check the evaluation output to understand performance metrics:

cat results/evaluation_report.json

The report will show metrics like proof success rate, average proof length, and computational efficiency.

Step 6: Interactive Testing

6.1 Test with Simple Theorem

Create a simple test script to interact with the model:

from prover_v2 import ProverV2

model = ProverV2(model_path='./models/prover_v2')
theorem = "∀ n : ℕ, n + 0 = n"
proof = model.prove_theorem(theorem)
print(f"Theorem: {theorem}")
print(f"Proof: {proof}")

This demonstrates how to use the model for actual theorem proving tasks.

6.2 Visualize Proof Search

Enable proof search visualization to understand recursive search behavior:

model.set_debug_mode(True)
proof = model.prove_theorem(theorem)
print(f"Search path: {model.get_search_path()}")

This shows how the model explores multiple proof paths recursively during the search process.

Summary

In this tutorial, you've learned how to set up the DeepSeek-Prover-V2 environment, prepare training data, train the model using recursive proof search techniques, and evaluate its performance on the MiniF2F benchmark. You've also gained hands-on experience with interactive theorem proving using the trained model. This implementation showcases how modern LLMs can be adapted for formal reasoning tasks, combining neural networks with traditional theorem proving methods to achieve impressive results in automated mathematical proof generation.