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arXiv Machine Learning By Jie Wu, Haoling Li, Xin Zhang, Jiani Guo, Jane Luo, Xuewei Yang, Steven Liu, Yangyu Huang, Ruihang Chu, Scarlett Li, Yujiu Yang

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

Read the original on arXiv Machine Learning →

The paper introduces X-Coder, a competitive programming model trained entirely on synthetic tasks, verified solutions, and reliable test cases, eliminating the need for real-world data in post‑training. A dual‑verification strategy is used to reduce noise in solutions and test outputs, providing high‑quality reward signals for reinforcement learning. X‑Coder‑14B achieves significant performance gains, scoring 67.5% on LiveCodeBench v5 and 63.4% on v6, surpassing its base model by over 40 points.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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