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--- |
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license: apache-2.0 |
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base_model: |
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B |
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--- |
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# Kaggle AI Mathematical Olympiad - Progress Prize 2 - 9th Place Solution (Fast-Math-R1-14B) |
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## Team |
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- Hiroshi Yoshihara @ [Aillis Inc.](https://aillis.jp/en), [The Univ. of Tokyo](https://publichealth.f.u-tokyo.ac.jp/#page_home) |
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- Yuichi Inoue @ [Sakana AI](https://sakana.ai) |
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- Taiki Yamaguchi @ [Rist Inc.](https://www.rist.co.jp/en/) |
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# Summary |
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By applying SFT and GRPO on difficult math problems, we enhanced the performance of `DeepSeek-R1-Distill-Qwen-14B` and developed `Fast-Math-R1-14B`, |
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which achieves up to 60% (on average approx. 30%) faster inference while maintaining accuracy. |
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Technical details can be found in [Kaggle Discussion](https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-2/discussion/571252) and [Github](https://github.com/analokmaus/kaggle-aimo2-fast-math-r1). |
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<img src="https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F1973217%2F4f221ab914f3e950fa35bdab5723d462%2Fpass1_aime_all.png?generation=1744851665782759&alt=media" max-height="300px"> |
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| | | AIME 2024 | | AIME 2025 | | |
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| ---------------------------- | ------------ | ---------------- | ------------- | ---------------- | ------------- | |
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| Model | Token budget | Pass@1 (avg. 64) | Output tokens | Pass@1 (avg. 64) | Output tokens | |
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| DeepSeek-R1-Distill-Qwen-14B | 16384 | 63.3 | 9590 | 46.7 | 10602 | |
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| | 12800 | 58 | 8632 | 41.9 | 9363 | |
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| | 8192 | 45.6 | 6638 | 30.6 | 6897 | |
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| Light-R1-14B-DS | 16384 | **66.8** | 10146 | **51.3** | 11308 | |
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| | 12800 | 59.2 | 9110 | 43.8 | 9834 | |
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| | 8192 | 42.4 | 7020 | 30.4 | 7124 | |
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| Fast-Math-R1-14B | 16384 | 66 | **7932** | 49.2 | **9066** | |
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| | 12800 | **63** | **7449** | **46.1** | **8282** | |
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| | 8192 | **51.4** | **5963** | **37.2** | **6256** | |
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| Fast-Math-R1-14B-SFT Only | 16384 | 65.2 | 10268 | 49.7 | 11264 | |
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| | 12800 | 57.2 | 9180 | 42.8 | 9805 | |
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| | 8192 | 41.3 | 7015 | 30.1 | 7074 | |
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# Dataset |
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- [Our first stage SFT dataset](https://huggingface.co/datasets/RabotniKuma/Fast-Math-R1-SFT) |
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- [Our second stage GRPO dataset](https://huggingface.co/datasets/RabotniKuma/Fast-Math-R1-GRPO) |
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# Inference |
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## vLLM |
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```python |
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from vllm import LLM, SamplingParams |
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vllm_engine = LLM( |
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model='RabotniKuma/Fast-Math-R1-14B', |
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max_model_len=8192, |
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gpu_memory_utilization=0.9, |
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trust_remote_code=True, |
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) |
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sampling_params = SamplingParams( |
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temperature=1.0, |
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top_p=0.90, |
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min_p=0.05, |
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max_tokens=8192, |
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stop='</think>', # Important: early stop at </think> to save output tokens |
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) |
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vllm_engine.generate('1+1=', sampling_params=sampling_params) |
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``` |
