DeepSeek AI Releases DeepSeekMath-V2: The Open Weights Maths Model That Scored 118/120 on Putnam 2024
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DeepSeek AI Releases DeepSeekMath-V2: The Open Weights Maths Model That Scored 118/120 on Putnam 2024
DeepSeek AI has released DeepSeekMath-V2, a 685B-parameter model that achieved 118 out of 120 points on Putnam 2024. The model uses self-verifying theorem proving to address gaps in prior AI math systems.
Why This Matters
Traditional math models reward only final answers, risking flawed reasoning that coincidentally produces correct results. DeepSeekMath-V2 prioritizes proof quality over answer accuracy, addressing structural flaws in competitions like the Putnam, where rigorous logic is essential. Human-labeled proofs showed that 20% of high-scoring AI answers contained critical reasoning errors, highlighting the cost of relying on final-answer metrics.
Key Insights
- “685B parameter model, 2025”: DeepSeekMath-V2 is built on DeepSeek-V3.2-Exp-Base and runs as a mixture of experts.
- “Verifier-first training”: The model uses Group Relative Policy Optimization (GRPO) to train a verifier that evaluates proof rigor, not just final scores.
- “Meta verification for hallucinations”: A secondary verifier ensures analyses don’t fabricate issues, raising meta-quality scores from 0.85 to 0.96.
Practical Applications
- Use Case: Math competition training using DeepSeekMath-V2 for proof generation and verification.
- Pitfall: Over-reliance on automated verification without human oversight may miss nuanced logical flaws in complex proofs.
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