README.md

metadata

language:
  - en
  - multilingual
license: apache-2.0
pipeline_tag: text-generation
tags:
  - physics
  - reinforcement-learning
  - olympiad
  - reasoning
  - competition
  - education
library_name: transformers

P1: Mastering Physics Olympiads with Reinforcement Learning

📚 Paper | 💻 Code | 🌐 P1 Project Page | 🏆 HiPhO Leaderboard

High-performance mid-scale model for physics reasoning

Model Description

P1-30B-A3B is the mid-size variant of the P1 series, a high-performance open-source language model specialized in physics reasoning. Built on Qwen3-30B-A3B-Thinking-2507 and refined through multi-stage reinforcement learning on curated physics competition data, P1-30B-A3B achieves impressive results while maintaining reasonable computational requirements, making it accessible for researchers working with physics problems.

Key Highlights

• 🥈 IPhO 2025 Silver-tier Performance: Strong competitive showing at international physics olympiad (18.5/30 points)
• 🥇 HiPhO Excellence: 8 gold medals, 4 silver medals, and 1 bronze medal across 13 physics contests

Performance Benchmarks

IPhO 2025 Results

| Model | Score | Medal | |:-----:|:-----:|:-----:|
| P1-30B-A3B | 18.5 | 🥈 Silver | | DeepSeek-R1 | 18.5 | 🥈 Silver | | Qwen3-235B-A22B-Thinking-2507 | 17.1 | 🥈 Silver | | Qwen3-30B-A3B-Thinking-2507 | 15.6 | 🥈 Silver |

HiPhO Comprehensive Results

| Category | P1-30B-A3B | Qwen3-235B-A22B | DeepSeek-R1 | Qwen3-30B-A3B (Base) | |:--------:|:----------:|:---------------:|:-----------:|:--------------------:|
| Overall Score | 32.5 | 33.5 | 32.9 | 29.9 | | Gold Medals (🥇) | 8 | 10 | 9 | 6 | | Silver Medals (🥈) | 4 | 3 | 3 | 6 | | Bronze Medals (🥉) | 1 | 0 | 1 | 1 | | Total Contests | 13 | 13 | 13 | 13 |

Generalization to STEM Tasks

Beyond physics reasoning, P1 improves across multiple domains. As shown below, P1-30B-A3B outperforms its base model Qwen3-30B-A3B-Thinking-2507 on math, coding, and STEM benchmarks, demonstrating strong generalization of physics reasoning.

| Model | AIME24 | AIME25 | HMMT | GPQA | HLE | LiveCodeBench | LiveBench |
|:-----:|:------:|:------:|:----:|:----:|:---:|:-------------:|:---------:|
| Qwen3-30B-A3B-Thinking-2507 (Base) | 90.4 | 85.0 | 71.3 | 73.0 | 11.6 | 66.7 | 76.6 | | P1-30B-A3B | 91.0 | 91.0 | 76.9 | 74.4 | 14.3 | 68.1 | 77.0 |

Usage

Basic Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "P1-30B-A3B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Physics problem solving
prompt = """Solve this physics problem:

A pendulum of length L = 1.0 m swings with small amplitude.
Calculate the period of oscillation and explain your reasoning.

Use g = 9.8 m/s²"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_length=81920,
    temperature=0.6,
    top_p=0.9,
    do_sample=True
)

solution = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(solution)

🙏 Acknowledgements

We are grateful to the open-source community for their invaluable contributions. Special thanks to:

• Qwen3 - for providing the foundational base models that powered our research
• slime - for their innovative work on efficient reinforcement learning framework that powered our training pipeline
• verl - for the versatile reinforcement learning framework that enabled our training pipeline
• sglang - for the efficient LLM serving and inference infrastructure
• Megatron-LM - for the large-scale model training framework

Citation

@misc{p1-2025,
    title={P1: Mastering Physics Olympiads with Reinforcement Learning},
    author={P1 Team},
    year={2025},
    url={https://huggingface.co/papers/2511.13612}
}