Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling
Authors: name: Yafu Li, name: Runzhe Zhan, name: Haoran Zhang, name: Shunkai Zhang, name: Yizhuo Li, name: Zhilin Wang, name: Jiacheng Chen, name: Futing Wang, name: Xuyang Hu, name: Yuchen Fan, name: Bangjie Xu, name: Yucheng Su, name: Xinmiao Han, name: Chenxi Li, name: Haodi Lei, name: Yufeng Zhao, name: Zejin Lin, name: Qianjia Cheng, name: Tong Zhu, name: Xiaoye Qu, name: Ganqu Cui, name: Peng Ye, name: Yun Luo, name: Zhouchen Lin, name: Yu Qiao, name: Bowen Zhou, name: Ning Ding, name: Yu Cheng

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.