Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu, Fuxing Leng, Guang Shi, Haobin Chen, Haoqi Fan, Jian Wang, Jianyu Jiang, Jiawei Wang, Jingji Chen, Jingjia Huang, Kang Lei, Liping Yuan, Lishu Luo, Pengfei Liu, Qinghao Ye, Rui Qian, Shen Yan, Shixiong Zhao, Shuai Peng, Shuangye Li, Sihang Yuan, Sijin Wu, Tianheng Cheng, Weiwei Liu, Wenqian Wang, Xianhan Zeng, Xiao Liu, Xiaobo Qin, Xiaohan Ding, Xiaojun Xiao, Xiaoying Zhang, Xuanwei Zhang, Xuehan Xiong, Yanghua Peng, Yangrui Chen, Yanwei Li, Yanxu Hu, Yi Lin, Yiyuan Hu, Yiyuan Zhang, Youbin Wu, Yu Li, Yudong Liu, Yue Ling, Yujia Qin, Zanbo Wang, Zhiwu He, Aoxue Zhang, Bairen Yi, Bencheng Liao, Can Huang, Can Zhang, Chaorui Deng, Chaoyi Deng, Cheng Lin, Cheng Yuan, Chenggang Li, Chenhui Gou, Chenwei Lou, Chengzhi Wei, Chundian Liu, Chunyuan Li, Deyao Zhu, Donghong Zhong, Feng Li, Feng Zhang, Gang Wu, Guodong Li, Guohong Xiao, Haibin Lin, Haihua Yang, Haoming Wang, Heng Ji, Hongxiang Hao, Hui Shen, Huixia Li, Jiahao Li, Jialong Wu, Jianhua Zhu, Jianpeng Jiao, Jiashi Feng, Jiaze Chen, Jianhui Duan, Jihao Liu, Jin Zeng, Jingqun Tang, Jingyu Sun, Joya Chen, Jun Long, Junda Feng, Junfeng Zhan, Junjie Fang, Junting Lu, Kai Hua, Kai Liu, Kai Shen, Kaiyuan Zhang, Ke Shen, Ke Wang, Keyu Pan, Kun Zhang, Kunchang Li, Lanxin Li, Lei Li, Lei Shi, Li Han, Liang Xiang, Liangqiang Chen, Lin Chen, Lin Li, Lin Yan, Liying Chi, Longxiang Liu, Mengfei Du, Mingxuan Wang, Ningxin Pan, Peibin Chen, Pengfei Chen, Pengfei Wu, Qingqing Yuan, Qingyao Shuai, Qiuyan Tao, Renjie Zheng, Renrui Zhang, Ru Zhang, Rui Wang, Rui Yang, Rui Zhao, Shaoqiang Xu, Shihao Liang, Shipeng Yan, Shu Zhong, Shuaishuai Cao, Shuangzhi Wu, Shufan Liu, Shuhan Chang, Songhua Cai, Tenglong Ao, Tianhao Yang, Tingting Zhang, Wanjun Zhong, Wei Jia, Wei Weng, Weihao Yu, Wenhao Huang, Wenjia Zhu, Wenli Yang, Wenzhi Wang, Xiang Long, XiangRui Yin, Xiao Li, Xiaolei Zhu, Xiaoying Jia, Xijin Zhang, Xin Liu, Xinchen Zhang, Xinyu Yang, Xiongcai Luo, Xiuli Chen, Xuantong Zhong, Xuefeng Xiao, Xujing Li, Yan Wu, Yawei Wen, Yifan Du, Yihao Zhang, Yining Ye, Yonghui Wu, Yu Liu, Yu Yue, Yufeng Zhou, Yufeng Yuan, Yuhang Xu, Yuhong Yang, Yun Zhang, Yunhao Fang, Yuntao Li, Yurui Ren, Yuwen Xiong, Zehua Hong, Zehua Wang, Zewei Sun, Zeyu Wang, Zhao Cai, Zhaoyue Zha, Zhecheng An, Zhehui Zhao, Zhengzhuo Xu, Zhipeng Chen, Zhiyong Wu, Zhuofan Zheng, Zihao Wang, Zilong Huang, Ziyu Zhu, Zuquan Song·May 11, 2025
Summary
Seed1.5-VL, a compact vision-language model, excels in multimodal understanding and reasoning, surpassing leading systems in tasks like GUI control and gameplay. Accessible on Volcano Engine, it showcases advancements in model design, data construction, and training techniques. The model's architecture includes a vision encoder, video encoding, and pre-training on diverse data. Post-training combines supervised fine-tuning and reinforcement learning, focusing on instruction-following and reasoning abilities. Performance is correlated with training loss on specific datasets, demonstrating log-linear relationships.
Introduction
Background
Overview of vision-language models
Importance of multimodal understanding in AI
Current state of the art in vision-language tasks
Objective
Highlighting Seed1.5-VL's advancements in model design, data construction, and training techniques
Emphasizing its superior performance in GUI control and gameplay tasks
Model Architecture
Vision Encoder
Description of the vision encoder component
How it processes visual inputs
Video Encoding
Explanation of video encoding techniques
Integration with the vision encoder for multimodal processing
Pre-training
Overview of pre-training process
Diverse data sources used for pre-training
Training Techniques
Supervised Fine-tuning
Description of supervised fine-tuning methods
Its role in enhancing model performance for specific tasks
Reinforcement Learning
Explanation of reinforcement learning in the context of Seed1.5-VL
Focus on instruction-following and reasoning abilities
Performance Evaluation
Correlation with Training Loss
Analysis of the relationship between training loss and performance
Log-linear relationships observed in specific datasets
Accessibility and Deployment
Volcano Engine
Overview of Volcano Engine as a platform for Seed1.5-VL
Benefits of using Volcano Engine for model deployment
Conclusion
Future Directions
Potential areas for further research and development
Expected advancements in vision-language models
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What are the key components of the Seed1.5-VL model's architecture?
How does Seed1.5-VL utilize supervised fine-tuning and reinforcement learning in its training process?
In what ways does Seed1.5-VL surpass leading systems in tasks like GUI control and gameplay?
What is the relationship between training loss and performance in Seed1.5-VL?