Skywork Open Reasoner 1 Technical Report
Authors: name: Jujie He; name: Jiacai Liu; name: Chris Yuhao Liu; name: Rui Yan; name: Chaojie Wang; name: Peng Cheng; name: Xiaoyu Zhang; name: Fuxiang Zhang; name: Jiacheng Xu; name: Wei Shen; name: Siyuan Li; name: Liang Zeng; name: Tianwen Wei; name: Cheng Cheng; name: Bo An; name: Yang Liu; name: Yahui Zhou
Published: 2025-05-28 12:56:04

Abstract: The success of DeepSeek-R1 underscores the significant role of reinforcement
learning (RL) in enhancing the reasoning capabilities of large language models
(LLMs). In this work, we present Skywork-OR1, an effective and scalable RL
implementation for long Chain-of-Thought (CoT) models. Building on the
DeepSeek-R1-Distill model series, our RL approach achieves notable performance
gains, increasing average accuracy across AIME24, AIME25, and LiveCodeBench
from 57.8% to 72.8% (+15.0%) for the 32B model and from 43.6% to 57.5% (+13.9%)
for the 7B model. Our Skywork-OR1-32B model surpasses both DeepSeek-R1 and
Qwen3-32B on the AIME24 and AIME25 benchmarks, while achieving comparable
results on LiveCodeBench. The Skywork-OR1-7B and Skywork-OR1-Math-7B models
demonstrate competitive reasoning capabilities among models of similar size. We
perform comprehensive ablation studies on the core components of our training
pipeline to validate their effectiveness. Additionally, we thoroughly
investigate the phenomenon of entropy collapse, identify key factors affecting
entropy dynamics, and demonstrate that mitigating premature entropy collapse is
critical for improved test performance. To support community research, we fully
open-source our model weights, training code, and training datasets.