README.md

---
license: mit
library_name: transformers
---

<p align="center">
  <img src="https://github.com/yinjjiew/Data/raw/main/cure/overviewplot.png" width="100%"/>
</p>


<p align="center">
  <img src="https://github.com/yinjjiew/Data/raw/main/cure/results.png" width="100%"/>
</p>





# Introduction to our ReasonFlux-Coders

We introduce **ReasonFlux-Coders**, trained with **CURE**, our algorithm for co-evolving an LLM's coding and unit test generation abilities.

* **ReasonFlux-Coder-7B** and **ReasonFlux-Coder-14B** outperform similarly sized Qwen Coders, DeepSeek Coders, and Seed-Coders, and naturally integrate into common test-time scaling and agentic coding pipelines.
* **ReasonFlux-Coder-4B** is our Long-CoT model, outperforming Qwen3-4B while achieving 64.8% efficiency in unit test generation. We have demonstrated its ability to serve as a reward model for training base models via reinforcement learning (see our [paper](https://arxiv.org/abs/2506.03136)).


[Paper](https://arxiv.org/abs/2506.03136) | [Code](https://github.com/Gen-Verse/CURE)

# Citation

```
@article{wang2025cure,
  title={Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning},
  author={Wang, Yinjie and Yang, Ling and Tian, Ye and Shen, Ke and Wang, Mengdi},
  journal={arXiv preprint arXiv:2506.03136},
  year={2025}
}
```