📄 论文总结
P1：通过强化学习掌握物理奥林匹克竞赛 / P1: Mastering Physics Olympiads with Reinforcement Learning
1️⃣ 一句话总结
这篇论文提出了一个名为P1的系列开源模型，它完全通过强化学习训练，在解决国际物理奥林匹克竞赛等高水平物理问题上表现卓越，甚至超越了人类金牌得主，同时展现出在数学和编程等其他推理任务上的强大通用能力。
🤖 系统
Abstract - P1: Mastering Physics Olympiads with Reinforcement Learning
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.