How DeepSeek Made The Best Math Prover Ever (+500% vs prev. SoTA)

AI is supposed to take over the world… but it still fumbles basic logic at times.

Despite all the hype around LLMs, when it comes to formal math where logic must be watertight and every proof step precise, most models still fall apart. But with the release of DeepSeek Prover V2, that might be changing.

In this post, we’ll dive deep into how this new model bridges the gap between language and logic, why most AI models struggle with math, and what DeepSeek is doing differently, from architecture tweaks to training data, from formal verification to theorem-solving benchmarks.

TL;DR

DeepSeek Prover V2 brings a few major upgrades that set it apart from earlier neural theorem provers.

• First, it uses a subgoal decomposition strategy. It breaks proofs into subgoals and solves them step by step instead of all at once.

• Second, the model bridges informal and formal reasoning. It mixes informal and formal reasoning which uses CoT to explain, then writes Lean proofs.

• Another key innovation is its data generation pipeline. It generates its own recursive proof loop of V3 (planner) and 7B prover model (executor).

• Lastly, its reinforcement learning setup is focused on reasoning, not just token prediction.

This makes the training process goal-driven, it pushes the model to become a better reasoner, not just a better guesser.

Before we dive into what makes V2 special, let’s rewind a bit.

What exactly is the problem it's solving? And how far have we come since the original DeepSeek Prover?

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