PRIME-RL/P1-30B-A3B

P1: Mastering Physics Olympiads with Reinforcement Learning

🌐 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://prime-rl.github.io/P1/}
}