P1-30B-A3B

<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

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HiPhO Comprehensive Results

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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.

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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://prime-rl.github.io/P1/}
}

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