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

Abstract:Competitive programming remains challenging for code LLMs. Despite recent progress, many training pipelines still depend on scarce real-world data, raising concerns about scalability and near-duplicate benchmark contamination. In this paper, we examine whether synthetic training artifacts can support the complete SFT-to-RL cycle for competitive programming: no real-world tasks, solutions, or test cases are directly used for post-training. To this end, we synthesize tasks, verified solutions, and reliable test cases that serve as reward signals for reinforcement learning. To improve reward reliability, we introduce a dual-verification strategy that reduces noise in both selected solutions and test outputs. Using this high-quality data, we train the X-Coder model series. X-Coder-14B achieves 67.5\% avg@8 on LiveCodeBench v5 and 63.4\% on v6, outperforming its base model by over 40 points. Further analysis provides practical insights into synthetic post-training, highlighting the value of diverse tasks, verified long-CoT supervision, and code-centric reinforcement. Our data and models are released at this https URL

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2601.06953 [cs.CL]

(or arXiv:2601.06953v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2601.06953 Focus to learn more arXiv-issued DOI via DataCite

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