rStar-Coder: Scaling Competitive Code Reasoning  with a  Large-Scale Verified  Dataset
Yifei Liu, Li Lyna Zhang, Yi Zhu, Bingcheng Dong, Xudong Zhou, Ning Shang, Fan Yang, Cheng Li, Mao Yang
Advancing code reasoning in large language models (LLMs) is fundamentally limited by the  scarcity of high-difficulty datasets, especially  those with verifiable input-output test cases necessary for rigorous solution validation at scale. We introduce rStar-Coder,  which significantly improves LLM code reasoning capabilities by constructing a large-scale, verified dataset of 418K competition-level code problems,  580K long-reasoning solutions  along with rich test cases of varying difficulty. This is achieved through three core contributions: (1) we curate competitive programming code problems and oracle solutions  to synthesize new, solvable problems; (2) we introduce a reliable input-output test case synthesis pipeline  that decouples the  generation  into a three-step input generation method and a mutual verification mechanism for effective output labeling; (3) we augment problems with high-quality, test-case-verified long-reasoning solutions. Extensive experiments on Qwen models (1.5B-14B) across various  code reasoning benchmarks demonstrate the superiority of rStar-Coder dataset, achieving leading performance comparable to frontier reasoning LLMs with much smaller model sizes. On LiveCodeBench, rStar-Coder improves Qwen2.5-7B from 17.4% to an impressive 57.3%, and Qwen2.5-14B from 23.3% to 62.5%, surpassing o3-mini (low) by 3.1%. On the more challenging USA Computing Olympiad, our 7B model achieves an average pass@1 accuracy of 16.15%, outperforming the frontier-level QWQ-32B.  We will release rStar-Coder dataset to advance open research in code reasoning.