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
).
@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}
}
Runs of Gen-Verse ReasonFlux-Coder-4B on huggingface.co
9
Total runs
0
24-hour runs
0
3-day runs
-1
7-day runs
0
30-day runs
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