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---
license: apache-2.0
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
---
# Kaggle AI Mathematical Olympiad - Progress Prize 2 - 9th Place Solution (Fast-Math-R1-14B)
## Team
- Hiroshi Yoshihara @ [Aillis Inc.](https://aillis.jp/en), [The Univ. of Tokyo](https://publichealth.f.u-tokyo.ac.jp/#page_home)
- Yuichi Inoue @ [Sakana AI](https://sakana.ai)
- Taiki Yamaguchi @ [Rist Inc.](https://www.rist.co.jp/en/)
# Summary
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`,
which achieves up to 60% (on average approx. 30%) faster inference while maintaining accuracy.
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).
<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">
| | | AIME 2024 | | AIME 2025 | |
| ---------------------------- | ------------ | ---------------- | ------------- | ---------------- | ------------- |
| Model | Token budget | Pass@1 (avg. 64) | Output tokens | Pass@1 (avg. 64) | Output tokens |
| DeepSeek-R1-Distill-Qwen-14B | 16384 | 63.3 | 9590 | 46.7 | 10602 |
| | 12800 | 58 | 8632 | 41.9 | 9363 |
| | 8192 | 45.6 | 6638 | 30.6 | 6897 |
| Light-R1-14B-DS | 16384 | **66.8** | 10146 | **51.3** | 11308 |
| | 12800 | 59.2 | 9110 | 43.8 | 9834 |
| | 8192 | 42.4 | 7020 | 30.4 | 7124 |
| Fast-Math-R1-14B | 16384 | 66 | **7932** | 49.2 | **9066** |
| | 12800 | **63** | **7449** | **46.1** | **8282** |
| | 8192 | **51.4** | **5963** | **37.2** | **6256** |
| Fast-Math-R1-14B-SFT Only | 16384 | 65.2 | 10268 | 49.7 | 11264 |
| | 12800 | 57.2 | 9180 | 42.8 | 9805 |
| | 8192 | 41.3 | 7015 | 30.1 | 7074 |
# Dataset
- [Our first stage SFT dataset](https://huggingface.co/datasets/RabotniKuma/Fast-Math-R1-SFT)
- [Our second stage GRPO dataset](https://huggingface.co/datasets/RabotniKuma/Fast-Math-R1-GRPO)
# Inference
## vLLM
```python
from vllm import LLM, SamplingParams
vllm_engine = LLM(
model='RabotniKuma/Fast-Math-R1-14B',
max_model_len=8192,
gpu_memory_utilization=0.9,
trust_remote_code=True,
)
sampling_params = SamplingParams(
temperature=1.0,
top_p=0.90,
min_p=0.05,
max_tokens=8192,
stop='</think>', # Important: early stop at </think> to save output tokens
)
vllm_engine.generate('1+1=', sampling_params=sampling_params)
``` |