---
license: apache-2.0
pipeline_tag: text-generation
---

# Model Card for Qwen2.5-7B-ECHO-MATH-GRPO

Based on Qwen2.5-7B, we trained the model with the ECHO framework using GRPO on the Eurus-2-RL-Math dataset.

It outperformed the Qwen2.5-32B on all six test datasets, achieving a 12% improvement on average.


Tabel 2: Model performance on math reasoning tasks. For AIME and AMC, the results are avg. @32
|  model    | AIME24 | AIME25 | AMC | MATH-500 | OlympiadBench | Minerva |  Avg. |
|----------------|---------------------------:|--------------------------:|--------------------------:|--------------------:|------------:|-------------:|-------------:|
| Qwen2.5-7B  | 2.7                      | 1.9                     | 22.0                     | 44.6               | 19.7        | 20.9         |18.6         |
| Qwen2.5-32B | 5.3                   | 2.1                    | 27.9                     | 62.4               | 25.4        | 33.5         |26.1         |
| Qwen2.5-32B-ECHO(GRPO)  | 13.1                      | 6.9                     | 45.6                     | 75.4               | 37.0         | 50.7         |38.1         |


# Quick start


```python
from transformers import pipeline

question = "math"
generator = pipeline("text-generation", model="GradientResearch/Qwen2.5-7B-ECHO-MATH-GRPO", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```



# Citation

If you find our work helpful, feel free to give us a cite.

```bibtex
@misc{xiao2025echodecouplinginferencetraining,
      title={Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms}, 
      author={Jie Xiao and Changyuan Fan and Qingnan Ren and Alfred Long and Yuchen Zhang and Rymon Yu and Eric Yang and Lynn Ai and Shaoduo Gan},
      year={2025},
      eprint={2508.05387},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2508.05387}, 
}
```