Primary evidence
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language ModelsS1 · Primary research · Paper · 2024-02-05
- DeepSeekMath code, evaluation and model release repositoryS1 · Primary research · Repository
The model continued pretraining on 120B math-related tokens and then used supervised and reinforcement-learning stages for mathematical reasoning.
DeepSeekMath-7B reached 51.7% on MATH without external tools or voting and introduced Group Relative Policy Optimization (GRPO).
It linked open math-model scaling with a verifier-friendly RL recipe that influenced subsequent reasoning systems.
Significance rationale: DeepSeekMath paired a strong open mathematical corpus pipeline with GRPO, a reinforcement-learning method that later became influential in reasoning-model training.
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