DeepSeek achieved high accuracy on the AIME 2024 benchmark by employing several innovative techniques:
1. Focused Training Data Generation: DeepSeek generated training data that could be automatically verified, particularly in domains like mathematics where correctness is unambiguous. This approach allowed them to focus on creating high-quality, relevant data that directly contributes to improving model performance[1].
2. Efficient Reward Functions: They developed highly efficient reward functions designed to identify which new training examples would actually improve the model. This strategy helped avoid wasting computational resources on redundant data, ensuring that the model learned from the most valuable examples[1].
3. Distillation and Model Optimization: DeepSeek used model distillation techniques to create smaller models that still achieved impressive results. For instance, their distilled 7B model surpassed the accuracy of larger open-source models like QwQ-32B-Preview, despite having fewer parameters. This demonstrates how focused training can lead to strong performance in specific domains with modest computational resources[1].
4. Test-Time Compute and Reasoning Chains: DeepSeek models, such as DeepSeek R1, utilize a technique called "test-time compute," which allows the model to spend more time and computational power on each problem. This approach mimics human-like deliberation, resulting in more accurate and thoughtful responses. As the model generates longer reasoning chains, it can solve increasingly complex problems with greater accuracy[6].
5. Transparency and Multi-Agent Architecture: DeepSeek models, particularly DeepSeek-R1, employ a multi-agent collaborative architecture that integrates diverse reasoning pathways. This synergy helps mitigate task-specific biases and enhances consistency by reducing variability. The structured approach allows the model to dynamically prioritize high-confidence solutions while iteratively refining less certain outputs[3].
These techniques collectively contribute to DeepSeek's impressive performance on the AIME 2024 benchmark, showcasing how strategic training methods and model design can outperform raw computational power in achieving high accuracy.
Citations:
[1] https://www.geekwire.com/2025/deepseeks-new-model-shows-that-ai-expertise-might-matter-more-than-compute-in-2025/
[2] https://www.reddit.com/r/LocalLLaMA/comments/1ibxhwp/deepseekr1distillqwen32b_2024_aime_i_performance/
[3] https://arxiv.org/html/2503.10573v1
[4] https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1
[5] https://aws.amazon.com/bedrock/deepseek
[6] https://smythos.com/ai-agents/agent-architectures/deepseek-r1/
[7] https://www.amitysolutions.com/blog/deepseek-r1-ai-giant-from-china
[8] https://cloudsecurityalliance.org/blog/2025/01/29/deepseek-rewriting-the-rules-of-ai-development
[9] https://www.inferless.com/learn/the-ultimate-guide-to-deepseek-models