Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning
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
683fa2ddbde0ae60c2f16183
Yinjie Wang
683fa2ddbde0ae60c2f16184
Ling Yang
claimed_verified
2025-06-27T08:58:08.674Z
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Ling Yang
user
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683fa2ddbde0ae60c2f16185
Ye Tian
claimed_verified
2025-09-12T16:25:04.835Z
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tyfeld
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Ke Shen
claimed_verified
2025-06-25T11:04:18.694Z
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shenke
user
shenke18
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Mengdi Wang
Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning
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
CURE, a reinforcement learning framework, improves code and unit test generation accuracy without ground-truth supervision, enhancing performance in various coding and testing tasks.
reinforcement learning reward design coding unit test generation ReasonFlux-Coder Qwen2.5-Instruct Qwen-Coder DeepSeek-Coder Seed-Coder test-time scaling agentic coding long-CoT inference efficiency reward model