title: Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning
summary: 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
paper_authors._id: 683fa2ddbde0ae60c2f16183
paper_authors.name: Yinjie Wang
paper_authors._id: 683fa2ddbde0ae60c2f16184
paper_authors.name: Ling Yang
paper_authors._id: 683fa2ddbde0ae60c2f16185
paper_authors.name: Ye Tian
paper_authors._id: 683fa2ddbde0ae60c2f16186
paper_authors.name: Ke Shen
paper_authors._id: 683fa2ddbde0ae60c2f16187
paper_authors.name: Mengdi Wang
paper_title: Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning
paper_summary: 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
paper_ai_summary: CURE, a reinforcement learning framework, improves code and unit test generation accuracy without ground-truth supervision, enhancing performance in various coding and testing tasks.
paper_ai_keywords: 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