Gen-Verse/ReasonFlux-Coder-4B
Gen-Verse/ReasonFlux-Coder-4B is a 4 billion parameter language model developed by Gen-Verse, specifically trained with the CURE algorithm to co-evolve coding and unit test generation abilities. This model excels at generating unit tests and serves as a Long-CoT model, outperforming Qwen3-4B in this domain. It is optimized for integration into test-time scaling and agentic coding pipelines, and can function as a reward model for reinforcement learning.
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ReasonFlux-Coder-4B: Co-Evolving Coding and Unit Test Generation
ReasonFlux-Coder-4B is a 4 billion parameter model from the ReasonFlux-Coders family, developed by Gen-Verse. It is uniquely trained using the CURE (Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning) algorithm, which simultaneously enhances the model's capabilities in both code generation and unit test creation.
Key Capabilities
- Enhanced Unit Test Generation: ReasonFlux-Coder-4B demonstrates 64.8% efficiency in unit test generation, outperforming models like Qwen3-4B in this specific task.
- Long-CoT Model: Designed as a Long-CoT (Chain-of-Thought) model, it is adept at handling complex reasoning tasks related to code.
- Integration with Coding Pipelines: The model is built for seamless integration into common test-time scaling and agentic coding workflows.
- Reinforcement Learning Reward Model: It has been demonstrated to effectively serve as a reward model for training base models via reinforcement learning, as detailed in the associated paper.
Good For
- Developers requiring robust unit test generation for their code.
- Applications involving agentic coding or test-time scaling.
- Research into reinforcement learning for LLM training, particularly for code-related tasks.