---
library_name: transformers
license: mit
pipeline_tag: text-generation
---
# 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)
## Introduction
We propose **CURE**, a novel reinforcement learning framework that co-evolves LLM coder and unit tester to improve the overall coding ability of large language models. Trained on just 4.5 K samples, our [ReasonFlux-Coder models](https://huggingface.co/collections/Gen-Verse/reasonflux-coder-6833109ed9300c62deb32c6b) outperform similarly sized Qwen Coder, DeepSeek Coder and Seed Coder. **Also, this is the first open-source Coding-RL project to make everything publicly available—including models, evaluation benchmarks, training and testing datasets, and training codes!**
## 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}
}
```