metadata
title: README
emoji: 🏃
colorFrom: blue
colorTo: indigo
sdk: static
pinned: false
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Evaluation
Through PRIME, we successfully achieve substantial improvements on key reasoning benchmarks over our SFT version of the model, leading to 16.7% improvement on average, and over 20% on AMC&AIME competitions. Our final model Eurus-2-7B-PRIME, based on Qwen-2.5-Math-7B-Base, surpassed its instruct version on 5 key reasoning benchmarks. The final results are presented below:
| Eurus-2-7B-PRIME | Eurus-2-7B-SFT | Qwen-2.5-Math-7B-Instruct | Llama-3.1-70B-Instruct | GPT-4o | |
|---|---|---|---|---|---|
| AIME 2024 | 26.7 (+23.3) | 3.3 | 13.3 | 16.7 | 9.3 |
| MATH-500 | 79.2 (+14.1) | 65.1 | 79.8 | 64.6 | 76.4 |
| AMC | 57.8 (+27.7) | 30.1 | 50.6 | 30.1 | 45.8 |
| Minerva Math | 38.6 (+5.9) | 32.7 | 34.6 | 35.3 | 36.8 |
| OlympiadBench | 42.1 (+12.3) | 29.8 | 40.7 | 31.9 | 43.3 |
| Avg. | 48.9 (+16.7) | 32.2 | 43.8 | 35.7 | 43.3 |
We achieved this with only 1/10 data and model resources compared with Qwen-Math.
| Eurus-2-7B-PRIME | Qwen2.5-Math-7B-Instruct | |
|---|---|---|
| Base Model | Qwen2.5-Math-7B | Qwen2.5-Math-7B |
| SFT Data | 230K (open-source) | 2.5M (open-source and in-house) |
| RM Data | 0 | 618K (in-house) |
| RM | Eurus-2-7B-SFT | Qwen2.5-Math-RM (72B) |
| RL Data | 150K queries \times 4 samples | 66K queries \times 32 samples |
Citation
If you find PRIME or ImplicitPRM helpful, please cite us.
@article{cui2025process,
title={Process reinforcement through implicit rewards},
author={Cui, Ganqu and Yuan, Lifan and Wang, Zefan and Wang, Hanbin and Li, Wendi and He, Bingxiang and Fan, Yuchen and Yu, Tianyu and Xu, Qixin and Chen, Weize and others},
journal={arXiv preprint arXiv:2502.01456},
year={2025}
}
@article{yuan2024implicitprm,
title={Free Process Rewards without Process Labels},
author={Lifan Yuan and Wendi Li and Huayu Chen and Ganqu Cui and Ning Ding and Kaiyan Zhang and Bowen Zhou and Zhiyuan Liu and Hao Peng},
journal={arXiv preprint arXiv:2412.01981},
year={2024}
}