P1: Mastering Physics Olympiads with Reinforcement Learning
Authors
Jiacheng Chen
Qianjia Cheng
Fangchen Yu
Haiyuan Wan
Yuchen Zhang
Shenghe Zheng
Junchi Yao
Qingyang Zhang
Haonan He
Yun Luo
Yufeng Zhao
Futing Wang
Li Sheng
Chengxing Xie
Yuxin Zuo
Yizhuo Li
Wenxauan Zeng
Yulun Wu
Rui Huang
Dongzhan Zhou
Kai Chen
Yu Qiao
Lei Bai
Yu Cheng
Ning Ding
Bowen Zhou
Peng Ye
Ganqu Cui
Abstract
Recent progress in large language models (LLMs) has moved the frontier from puzzle-solving to science-grade reasoning-the kind needed to tackle problems whose answers must stand against nature, not merely fit a rubric. Physics is the sharpest test of this shift, which binds symbols to reality in a fundamental way, serving as the cornerstone of most modern technologies. In this work, we manage to advance physics research by developing large language models with exceptional physics reasoning capabilities, especially excel at solving Olympiad-level physics problems. We introduce P1, a family of open-source physics reasoning models trained entirely through reinforcement learning (RL). Among them, P1-235B-A22B is the first open-source model with Gold-medal performance at the latest International Physics Olympiad (IPhO 2025), and wins 12 gold medals out of 13 international/regional physics competitions in 2024/2025. P1-30B-A3B also surpasses almost all other open-source models on IPhO 2025, getting a silver medal. Further equipped with an agentic framework PhysicsMinions, P1-235B-A22B+PhysicsMinions achieves overall No.1 on IPhO 2025, and obtains the highest average score over the 13 physics competitions. Besides physics, P1 models also present great performance on other reasoning tasks like math and coding, showing the great generalibility of P1 series.
