Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.01718 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912801813954560 |
|---|---|
| author | ai, YuanLab. : Wu, Shawn Wang, Sean Li, Louie Chen, Darcy Wang, Allen Luo, Jiangang Zhao, Xudong Shen, Joseph Ma, Gawain Jia, Jasper Mao, Marcus Wang, Claire He, Hunter Wang, Carol Zhang, Zera Wang, Jason Shen, Chonly Zhang, Leo Chen, Logan Meng, Qasim Gong, James Zhao, Danied Zheng, Penn Zhu, Owen Yu, Tong |
| author_facet | ai, YuanLab. : Wu, Shawn Wang, Sean Li, Louie Chen, Darcy Wang, Allen Luo, Jiangang Zhao, Xudong Shen, Joseph Ma, Gawain Jia, Jasper Mao, Marcus Wang, Claire He, Hunter Wang, Carol Zhang, Zera Wang, Jason Shen, Chonly Zhang, Leo Chen, Logan Meng, Qasim Gong, James Zhao, Danied Zheng, Penn Zhu, Owen Yu, Tong |
| contents | We introduce Yuan3.0 Flash, an open-source Mixture-of-Experts (MoE) MultiModal Large Language Model featuring 3.7B activated parameters and 40B total parameters, specifically designed to enhance performance on enterprise-oriented tasks while maintaining competitive capabilities on general-purpose tasks. To address the overthinking phenomenon commonly observed in Large Reasoning Models (LRMs), we propose Reflection-aware Adaptive Policy Optimization (RAPO), a novel RL training algorithm that effectively regulates overthinking behaviors. In enterprise-oriented tasks such as retrieval-augmented generation (RAG), complex table understanding, and summarization, Yuan3.0 Flash consistently achieves superior performance. Moreover, it also demonstrates strong reasoning capabilities in domains such as mathematics, science, etc., attaining accuracy comparable to frontier model while requiring only approximately 1/4 to 1/2 of the average tokens. Yuan3.0 Flash has been fully open-sourced to facilitate further research and real-world deployment: https://github.com/Yuan-lab-LLM/Yuan3.0. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01718 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Yuan3.0 Flash: An Open Multimodal Large Language Model for Enterprise Applications ai, YuanLab. : Wu, Shawn Wang, Sean Li, Louie Chen, Darcy Wang, Allen Luo, Jiangang Zhao, Xudong Shen, Joseph Ma, Gawain Jia, Jasper Mao, Marcus Wang, Claire He, Hunter Wang, Carol Zhang, Zera Wang, Jason Shen, Chonly Zhang, Leo Chen, Logan Meng, Qasim Gong, James Zhao, Danied Zheng, Penn Zhu, Owen Yu, Tong Artificial Intelligence We introduce Yuan3.0 Flash, an open-source Mixture-of-Experts (MoE) MultiModal Large Language Model featuring 3.7B activated parameters and 40B total parameters, specifically designed to enhance performance on enterprise-oriented tasks while maintaining competitive capabilities on general-purpose tasks. To address the overthinking phenomenon commonly observed in Large Reasoning Models (LRMs), we propose Reflection-aware Adaptive Policy Optimization (RAPO), a novel RL training algorithm that effectively regulates overthinking behaviors. In enterprise-oriented tasks such as retrieval-augmented generation (RAG), complex table understanding, and summarization, Yuan3.0 Flash consistently achieves superior performance. Moreover, it also demonstrates strong reasoning capabilities in domains such as mathematics, science, etc., attaining accuracy comparable to frontier model while requiring only approximately 1/4 to 1/2 of the average tokens. Yuan3.0 Flash has been fully open-sourced to facilitate further research and real-world deployment: https://github.com/Yuan-lab-LLM/Yuan3.0. |
| title | Yuan3.0 Flash: An Open Multimodal Large Language Model for Enterprise Applications |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.01718 |