# 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} } ```