推理模型的最新进展显著提升了长程数学与科学问题的求解能力，目前已有多个系统在国际数学奥林匹克（IMO）和国际物理奥林匹克（IPhO）题目上达到金牌水平。本文提出一种简洁统一的方案，可将经过后训练的推理主干模型转化为严谨的奥林匹克级解题器。
该方案首先采用基于反向困惑度课程（reverse-perplexity curriculum）的监督微调（SFT），以注入严谨的证明搜索与自我验证行为；随后通过两阶段强化学习（RL）流水线扩展这些行为——从基于可验证奖励的强化学习逐步过渡到更精细的证明级强化学习；最后借助测试时扩展（test-time scaling）提升解题性能。
应用该方案，我们以约34万条长度低于8000 token的轨迹对300亿参数（A3B架构）主干模型进行监督微调，随后执行200步强化学习。由此得到的模型SU-01能够稳定处理轨迹长度超过10万token的难题，并在数学与物理奥林匹克竞赛（包括IMO 2025/USAMO 2026及IPhO 2024/2025）中达到金牌水平。
此外，该模型在数学与物理之外的领域也展现出强大的科学推理泛化能力。
Recent progress in reasoning models has substantially advanced long-horizon mathematical and scientific problem solving, with several systems now reaching gold-medal-level performance on International Mathematical Olympiad (IMO) and International Physics Olympiad (IPhO) problems. In this paper, we introduce a simple and unified recipe for converting a post-trained reasoning backbone into a rigorous olympiad-level solver. The recipe first uses a reverse-perplexity curriculum for SFT to instill rigorous proof-search and self-checking behaviors, then scales these behaviors through a two-stage RL pipeline that progresses from RL with verifiable rewards to more delicate proof-level RL, and finally boosts solving performance with test-time scaling.
Applying this recipe, we train a 30B-A3B backbone with SFT on around 340K sub-8K-token trajectories followed by 200 RL steps. The resulting model, SU-01, supports stable reasoning on difficult problems with trajectories exceeding 100K tokens, while achieving gold-medal-level performance on mathematical and physical olympiad competitions, including IMO 2025/USAMO 2026 and IPhO 2024/2025. It also demonstrates strong generalization of scientific reasoning to domains beyond mathematics and physics.