| library_name: transformers | |
| license: mit | |
| pipeline_tag: text-generation | |
| <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} | |
| } | |
| ``` |