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

<div align="center">
  <h1 style="font-size: 2em; font-weight: bold;">P1: Mastering Physics Olympiads with Reinforcement Learning</h1>
</div>

<p align="center">
  <a href="https://prime-rl.github.io/P1/"><b>🌐 P1 Project Page</b></a> |
  <a href="https://phyarena.github.io/"><b>🏆 HiPhO Leaderboard</b></a>
</p>

<p align="center">
<img src="https://raw.githubusercontent.com/PRIME-RL/P1/main/docs/imgs/Score_IPhO_2025_P1_v2.jpg" style="width: 800px" align=center>
</p>

<p align="center">
<i>High-performance mid-scale model for physics reasoning</i>       
</p>

## 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

<div align="center">

| 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** |
</div>

### HiPhO Comprehensive Results

<div align="center">

| 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 |

</div>

### 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.

<div align="center">

| 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** |

</div>


## Usage

### Basic Inference

```python
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](https://huggingface.co/collections/Qwen/qwen3)** - for providing the foundational base models that powered our research
- **[slime](https://github.com/THUDM/slime)** - for their innovative work on efficient reinforcement learning framework that powered our training pipeline
- **[verl](https://github.com/volcengine/verl)** - for the versatile reinforcement learning framework that enabled our training pipeline
- **[sglang](https://github.com/sgl-project/sglang)** - for the efficient LLM serving and inference infrastructure
- **[Megatron-LM](https://github.com/NVIDIA/Megatron-LM)** - for the large-scale model training framework

## Citation

```bibtex
@misc{p1-2025,
    title={P1: Mastering Physics Olympiads with Reinforcement Learning},
    author={P1 Team},
    year={2025},
    url={https://prime-rl.github.io/P1/}
}
```

</div>