Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning
Authors: name: Yinjie Wang, name: Ling Yang, name: Ye Tian, name: Ke Shen, name: Mengdi Wang

We propose CURE, a novel reinforcement learning framework with a dedicated
reward design that co-evolves coding and unit test generation capabilities
based on their interaction outcomes, without any ground-truth code as
supervision. This approach enables flexible and scalable training and allows
the unit tester to learn directly from the coder's mistakes. Our derived
ReasonFlux-Coder-7B and 14B models improve code generation accuracy by 5.3% and
Best-of-N accuracy by 9.0% after optimization on Qwen2.5-Instruct models,
outperforming similarly sized Qwen-Coder, DeepSeek-Coder, and Seed-Coder. They
naturally extend to downstream tasks such as test-time scaling and agentic
coding-achieving a 8.1% improvement over the base model. For the long-CoT
model, our ReasonFlux-Coder-4B consistently outperforms Qwen3-4B while
achieving 64.8% inference efficiency in unit test generation. Notably, we also
find that our model can serve as an effective reward model for reinforcement
learning on base models. Project: https://github.com/Gen-Verse/CURE