The performance of DeepSeek-R1 on the AIMO2 dataset is not explicitly detailed in the available information, but we can infer its capabilities based on its performance in similar mathematical benchmarks and the improvements observed in related datasets.
1. Mathematical Performance: DeepSeek-R1 has shown strong performance in mathematical tasks, achieving a 79.8% accuracy on the AIME 2024 benchmark and 97.3% on MATH-500[1][2][5]. This suggests that it is highly proficient in handling complex mathematical problems.
2. AIMO2 Dataset: While specific results on AIMO2 are not provided, there is mention of notable improvements in performance over closed, unpublished datasets like AIMO2, indicating that DeepSeek-R1 models are exceptionally proficient in mathematics[4]. The AIMO2 dataset, being a math competition with problems categorized between AIME and IMO difficulty levels, likely benefits from DeepSeek-R1's advanced mathematical reasoning capabilities.
3. Comparison to Other Models: DeepSeek-R1 generally matches or surpasses the performance of models like OpenAI o1 in various benchmarks[1][2]. However, specific comparisons on AIMO2 are not available. The model's efficiency and speed, thanks to its MoE architecture, might also contribute to better performance in processing complex mathematical tasks compared to other models[5][6].
4. Distilled Models: DeepSeek-R1's distilled models, such as DeepSeek-R1-Distill-Qwen-32B, have shown impressive results on mathematical benchmarks like AIME 2024, achieving a 72.6% pass rate[1]. This suggests that even the distilled versions of DeepSeek-R1 maintain strong mathematical capabilities, which could translate well to datasets like AIMO2.
In summary, while specific performance metrics for DeepSeek-R1 on the AIMO2 dataset are not provided, its strong performance in similar mathematical benchmarks and improvements in related datasets suggest it would likely perform well on AIMO2. Its MoE architecture and efficiency also position it as a competitive model in mathematical reasoning tasks.
Citations:
[1] https://huggingface.co/deepseek-ai/DeepSeek-R1
[2] https://www.datacamp.com/blog/deepseek-r1
[3] https://writesonic.com/blog/deepseek-vs-chatgpt
[4] https://www.reddit.com/r/LocalLLaMA/comments/1ibxhwp/deepseekr1distillqwen32b_2024_aime_i_performance/
[5] https://writesonic.com/blog/deepseek-r1-review
[6] https://www.byteplus.com/en/topic/385090
[7] https://github.com/deepseek-ai/DeepSeek-R1
[8] https://www.reddit.com/r/LocalLLaMA/comments/1i8rujw/notes_on_deepseek_r1_just_how_good_it_is_compared/