用开源模型在编程竞赛中首次拿下金牌，靠的是测试时扩展计算资源。
Scaling Test-Time Compute to Achieve IOI Gold Medal with Open-Weight Models
- 通过生成、聚类、排序和轮赛提交，高效探索解空间。
- 在有限验证预算下，性能随计算资源提升而稳定增长。
- 首次用开源模型实现IOI2025金牌，推动可复现推理评估。
竞赛编程已成为评估大语言模型推理与解题能力的严格基准。国际信息学奥林匹克竞赛（IOI）是年度最权威的编程竞赛之一，成为衡量人类与人工智能编程能力的关键指标。尽管一些专有模型声称达到金牌水平，但方法常不公开；而使用开源模型达成同等表现仍面临重大挑战。本文提出GenCluster，一种可扩展且可复现的测试时计算框架，实现了使用开源模型在IOI上获得金牌级表现。该方法结合大规模生成、行为聚类、排序及轮赛提交策略，在有限验证预算下高效探索多样解空间。实验表明，所提方法性能随可用计算资源持续提升，缩小了开放系统与封闭系统间的差距。特别地，我们将首次展示使用开源模型gpt-oss-120b在IOI 2025中获得金牌，为大语言模型推理能力的透明化、可复现评估树立新标杆。
原文摘要 · Abstract (English)
Competitive programming has become a rigorous benchmark for evaluating the reasoning and problem-solving capabilities of large language models (LLMs). The International Olympiad in Informatics (IOI) stands out as one of the most prestigious annual competitions in competitive programming and has become a key benchmark for comparing human and AI-level programming ability. While several proprietary models have been claimed to achieve gold medal-level performance at the IOI, often with undisclosed methods, achieving comparable results with open-weight models remains a significant challenge. In this paper, we present GenCluster, a scalable and reproducible test-time compute framework that attains IOI gold-level performance using open-weight models. It combines large-scale generation, behavioral clustering, ranking, and a round-robin submission strategy to efficiently explore diverse solution spaces under limited validation budgets. Our experiments show that the performance of our proposed approach scales consistently with available compute, narrowing the gap between open and closed systems. Notably, we will show that GenCluster can achieve a gold medal at IOI 2025 for the first time with an open-weight model gpt-oss-120b, setting a new benchmark for transparent and reproducible evaluation of reasoning in LLMs
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