P1: Mastering Physics Olympiads with Reinforcement Learning

TL;DR

This paper introduces P1, a family of open-source physics reasoning models trained with reinforcement learning, achieving top performance in physics Olympiads and demonstrating strong generalization to math and coding tasks.

Contribution

The paper presents the first open-source physics reasoning models trained via reinforcement learning that attain top Olympiad-level performance and generalize across reasoning domains.

Findings

P1-235B-A22B achieves Gold at IPhO 2025.

P1 models outperform other open-source models on physics competitions.

P1 models also excel in math and coding reasoning tasks.

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.