In this episode, we dissect DeepSeek Prover V2, an open-source large language model pushing the boundaries of AI in formal theorem proving using Lean 4.
We unpack its innovative "cold start" training procedure, where the general-purpose DeepSeek-V3 is ingeniously used to generate initial training data by recursively decomposing complex problems into manageable subgoals.
Discover how this approach synthesizes informal, human-like mathematical intuition with the rigorous, step-by-step logic required for formal proofs.
We'll explore the architecture of the 671 billion parameter model, its two-stage training process creating distinct 'Chain-of-Thought' (CoT) and 'non-CoT' modes, and its state-of-the-art performance on challenging benchmarks like MiniF2F, PutnamBench, and the newly introduced ProverBench (which includes problems from AIME competitions). Learn about the significance of its recursive proof search, curriculum learning, and reasoning-oriented reinforcement learning, all aimed at bridging the gap between intuitive reasoning and formal mathematical verification.
Join us as we explore why DeepSeek Prover V2 represents a major stride in AI's ability to tackle complex mathematical logic.
Please also checkout our previous episode for DeepSeek V3 in YouTube, Spotify and Apple Podcast.
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