X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests

• URL: http://arxiv.org/abs/2601.06953v1
• Date: Sun, 11 Jan 2026 15:22:33 GMT
• Title: X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests
• Authors: Jie Wu, Haoling Li, Xin Zhang, Jiani Guo, Jane Luo, Steven Liu, Yangyu Huang, Ruihang Chu, Scarlett Li, Yujiu Yang,
• Abstract summary: We propose a fully synthetic approach to training Code LLMs with entirely generated tasks, solutions, and test cases.<n>To support this, we leverage feature-based synthesis to propose a novel data synthesis pipeline called SynthSmith.<n> SynthSmith shows strong potential in producing diverse and challenging tasks, along with verified solutions and tests.<n>X-Coder model series achieves a notable pass rate of 62.9 avg@8 on LiveCodeBench v5 and 55.8 on v6, outperforming DeepCoder-14B-Preview and AReal-boba2-14B.
• Score: 47.271827881215295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Competitive programming presents great challenges for Code LLMs due to its intensive reasoning demands and high logical complexity. However, current Code LLMs still rely heavily on real-world data, which limits their scalability. In this paper, we explore a fully synthetic approach: training Code LLMs with entirely generated tasks, solutions, and test cases, to empower code reasoning models without relying on real-world data. To support this, we leverage feature-based synthesis to propose a novel data synthesis pipeline called SynthSmith. SynthSmith shows strong potential in producing diverse and challenging tasks, along with verified solutions and tests, supporting both supervised fine-tuning and reinforcement learning. Based on the proposed synthetic SFT and RL datasets, we introduce the X-Coder model series, which achieves a notable pass rate of 62.9 avg@8 on LiveCodeBench v5 and 55.8 on v6, outperforming DeepCoder-14B-Preview and AReal-boba2-14B despite having only 7B parameters. In-depth analysis reveals that scaling laws hold on our synthetic dataset, and we explore which dimensions are more effective to scale. We further provide insights into code-centric reinforcement learning and highlight the key factors that shape performance through detailed ablations and analysis. Our findings demonstrate that scaling high-quality synthetic data and adopting staged training can greatly advance code reasoning, while mitigating reliance on real-world coding data.

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