metadata
license: mit
library_name: transformers
Introduction to our ReasonFlux-Coders
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).
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