Buchumschlag

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Li, Yafu, Zhan, Runzhe, Zhang, Haoran, Zhang, Shunkai, Li, Yizhuo, Wang, Zhilin, Chen, Jiacheng, Wang, Futing, Hu, Xuyang, Fan, Yuchen, Xu, Bangjie, Su, Yucheng, Han, Xinmiao, Li, Chenxi, Lei, Haodi, Zhao, Yufeng, Lin, Zejin, Cheng, Qianjia, Zhu, Tong, Qu, Xiaoye, Cui, Ganqu, Ye, Peng, Luo, Yun, Lin, Zhouchen, Qiao, Yu, Zhou, Bowen, Ding, Ning, Cheng, Yu
Format: Preprint
Veröffentlicht: 2026
Schlagworte:
Artificial Intelligence
Computation and Language
Online-Zugang:https://arxiv.org/abs/2605.13301
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Inhaltsangabe:
  • 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.

Ähnliche Einträge