**ERNIE 5.0 技术报告**

ERNIE 5.0 Technical Report

February 4, 2026

作者: Haifeng Wang, Hua Wu, Tian Wu, Yu Sun, Jing Liu, Dianhai Yu, Yanjun Ma, Jingzhou He, Zhongjun He, Dou Hong, Qiwen Liu, Shuohuan Wang, Junyuan Shang, Zhenyu Zhang, Yuchen Ding, Jinle Zeng, Jiabin Yang, Liang Shen, Ruibiao Chen, Weichong Yin, Siyu Ding, Dai Dai, Shikun Feng, Siqi Bao, Bolei He, Yan Chen, Zhenyu Jiao, Ruiqing Zhang, Zeyu Chen, Qingqing Dang, Kaipeng Deng, Jiajun Jiang, Enlei Gong, Guoxia Wang, Yanlin Sha, Yi Liu, Yehan Zheng, Weijian Xu, Jiaxiang Liu, Zengfeng Zeng, Yingqi Qu, Zhongli Li, Zhengkun Zhang, Xiyang Wang, Zixiang Xu, Xinchao Xu, Zhengjie Huang, Dong Wang, Bingjin Chen, Yue Chang, Xing Yuan, Shiwei Huang, Qiao Zhao, Xinzhe Ding, Shuangshuang Qiao, Baoshan Yang, Bihong Tang, Bin Li, Bingquan Wang, Binhan Tang, Binxiong Zheng, Bo Cui, Bo Ke, Bo Zhang, Bowen Zhang, Boyan Zhang, Boyang Liu, Caiji Zhang, Can Li, Chang Xu, Chao Pang, Chao Zhang, Chaoyi Yuan, Chen Chen, Cheng Cui, Chenlin Yin, Chun Gan, Chunguang Chai, Chuyu Fang, Cuiyun Han, Dan Zhang, Danlei Feng, Danxiang Zhu, Dong Sun, Dongbo Li, Dongdong Li, Dongdong Liu, Dongxue Liu, Fan Ding, Fan Hu, Fan Li, Fan Mo, Feisheng Wu, Fengwei Liu, Gangqiang Hu, Gaofeng Lu, Gaopeng Yong, Gexiao Tian, Guan Wang, Guangchen Ni, Guangshuo Wu, Guanzhong Wang, Guihua Liu, Guishun Li, Haibin Li, Haijian Liang, Haipeng Ming, Haisu Wang, Haiyang Lu, Haiye Lin, Han Zhou, Hangting Lou, Hanwen Du, Hanzhi Zhang, Hao Chen, Hao Du, Hao Liu, Hao Zhou, Haochen Jiang, Haodong Tian, Haoshuang Wang, Haozhe Geng, Heju Yin, Hong Chen, Hongchen Xue, Hongen Liu, Honggeng Zhang, Hongji Xu, Hongwei Chen, Hongyang Zhang, Hongyuan Zhang, Hua Lu, Huan Chen, Huan Wang, Huang He, Hui Liu, Hui Zhong, Huibin Ruan, Jiafeng Lu, Jiage Liang, Jiahao Hu, Jiahao Hu, Jiajie Yang, Jialin Li, Jian Chen, Jian Wu, Jianfeng Yang, Jianguang Jiang, Jianhua Wang, Jianye Chen, Jiaodi Liu, Jiarui Zhou, Jiawei Lv, Jiaxin Zhou, Jiaxuan Liu, Jie Han, Jie Sun, Jiefan Fang, Jihan Liu, Jihua Liu, Jing Hu, Jing Qian, Jing Yan, Jingdong Du, Jingdong Wang, Jingjing Wu, Jingyong Li, Jinheng Wang, Jinjin Li, Jinliang Lu, Jinlin Yu, Jinnan Liu, Jixiang Feng, Jiyi Huang, Jiyuan Zhang, Jun Liang, Jun Xia, Jun Yu, Junda Chen, Junhao Feng, Junhong Xiang, Junliang Li, Kai Liu, Kailun Chen, Kairan Su, Kang Hu, Kangkang Zhou, Ke Chen, Ke Wei, Kui Huang, Kun Wu, Kunbin Chen, Lei Han, Lei Sun, Lei Wen, Linghui Meng, Linhao Yu, Liping Ouyang, Liwen Zhang, Longbin Ji, Longzhi Wang, Meng Sun, Meng Tian, Mengfei Li, Mengqi Zeng, Mengyu Zhang, Ming Hong, Mingcheng Zhou, Mingming Huang, Mingxin Chen, Mingzhu Cai, Naibin Gu, Nemin Qiu, Nian Wang, Peng Qiu, Peng Zhao, Pengyu Zou, Qi Wang, Qi Xin, Qian Wang, Qiang Zhu, Qianhui Luo, Qianwei Yang, Qianyue He, Qifei Wu, Qinrui Li, Qiwen Bao, Quan Zhang, Quanxiang Liu, Qunyi Xie, Rongrui Zhan, Rufeng Dai, Rui Peng, Ruian Liu, Ruihao Xu, Ruijie Wang, Ruixi Zhang, Ruixuan Liu, Runsheng Shi, Ruting Wang, Senbo Kang, Shan Lu, Shaofei Yu, Shaotian Gong, Shenwei Hu, Shifeng Zheng, Shihao Guo, Shilong Fan, Shiqin Liu, Shiwei Gu, Shixi Zhang, Shuai Yao, Shuang Zhang, Shuangqiao Liu, Shuhao Liang, Shuwei He, Shuwen Yang, Sijun He, Siming Dai, Siming Wu, Siyi Long, Songhe Deng, Suhui Dong, Suyin Liang, Teng Hu, Tianchan Xu, Tianliang Lv, Tianmeng Yang, Tianyi Wei, Tiezhu Gao, Ting Sun, Ting Zhang, Tingdan Luo, Wei He, Wei Luan, Wei Yin, Wei Zhang, Wei Zhou, Weibao Gong, Weibin Li, Weicheng Huang, Weichong Dang, Weiguo Zhu, Weilong Zhang, Weiqi Tan, Wen Huang, Wenbin Chang, Wenjing Du, Wenlong Miao, Wenpei Luo, Wenquan Wu, Xi Shi, Xi Zhao, Xiang Gao, Xiangguo Zhang, Xiangrui Yu, Xiangsen Wang, Xiangzhe Wang, Xianlong Luo, Xianying Ma, Xiao Tan, Xiaocong Lin, Xiaofei Wang, Xiaofeng Peng, Xiaofeng Wu, Xiaojian Xu, Xiaolan Yuan, Xiaopeng Cui, Xiaotian Han, Xiaoxiong Liu, Xiaoxu Fei, Xiaoxuan Wu, Xiaoyu Wang, Xiaoyu Zhang, Xin Sun, Xin Wang, Xinhui Huang, Xinming Zhu, Xintong Yu, Xinyi Xu, Xinyu Wang, Xiuxian Li, XuanShi Zhu, Xue Xu, Xueying Lv, Xuhong Li, Xulong Wei, Xuyi Chen, Yabing Shi, Yafeng Wang, Yamei Li, Yan Liu, Yanfu Cheng, Yang Gao, Yang Liang, Yang Wang, Yang Wang, Yang Yang, Yanlong Liu, Yannian Fu, Yanpeng Wang, Yanzheng Lin, Yao Chen, Yaozong Shen, Yaqian Han, Yehua Yang, Yekun Chai, Yesong Wang, Yi Song, Yichen Zhang, Yifei Wang, Yifeng Guo, Yifeng Kou, Yilong Chen, Yilong Guo, Yiming Wang, Ying Chen, Ying Wang, Yingsheng Wu, Yingzhan Lin, Yinqi Yang, Yiran Xing, Yishu Lei, Yixiang Tu, Yiyan Chen, Yong Zhang, Yonghua Li, Yongqiang Ma, Yongxing Dai, Yongyue Zhang, Yu Ran, Yu Sun, Yu-Wen Michael Zhang, Yuang Liu, Yuanle Liu, Yuanyuan Zhou, Yubo Zhang, Yuchen Han, Yucheng Wang, Yude Gao, Yuedong Luo, Yuehu Dong, Yufeng Hu, Yuhui Cao, Yuhui Yun, Yukun Chen, Yukun Gao, Yukun Li, Yumeng Zhang, Yun Fan, Yun Ma, Yunfei Zhang, Yunshen Xie, Yuping Xu, Yuqin Zhang, Yuqing Liu, Yurui Li, Yuwen Wang, Yuxiang Lu, Zefeng Cai, Zelin Zhao, Zelun Zhang, Zenan Lin, Zezhao Dong, Zhaowu Pan, Zhaoyu Liu, Zhe Dong, Zhe Zhang, Zhen Zhang, Zhengfan Wu, Zhengrui Wei, Zhengsheng Ning, Zhenxing Li, Zhenyu Li, Zhenyu Qian, Zhenyun Li, Zhi Li, Zhichao Chen, Zhicheng Dong, Zhida Feng, Zhifan Feng, Zhihao Deng, Zhijin Yu, Zhiyang Chen, Zhonghui Zheng, Zhuangzhuang Guo, Zhujun Zhang, Zhuo Sun, Zichang Liu, Zihan Lin, Zihao Huang, Zihe Zhu, Ziheng Zhao, Ziping Chen, Zixuan Zhu, Ziyang Xu, Ziyi Liang, Ziyuan Gao

cs.AI

摘要

在本报告中，我们推出ERNIE 5.0——一个原生自回归的基础模型，专为跨文本、图像、视频和音频的统一多模态理解与生成而设计。所有模态均基于超稀疏专家混合（MoE）架构，采用模态无关的专家路由机制，在统一的「下一组标记预测」目标下从头开始训练。为应对多样化资源约束下大规模部署的实际挑战，ERNIE 5.0采用创新的弹性训练范式：在单次预训练过程中，模型可学习具有不同深度、专家容量和路由稀疏度的子模型家族，从而在内存或时间受限场景中实现性能、模型规模与推理延迟间的灵活权衡。此外，我们系统性地解决了将强化学习扩展至统一基础模型的挑战，确保在超稀疏MoE架构与多样化多模态设置下实现高效稳定的训练后优化。大量实验表明，ERNIE 5.0在多种模态上均展现出强劲且均衡的性能。据我们所知，在公开披露的模型中，ERNIE 5.0是首个实现生产级规模的万亿参数统一自回归模型，同时支持多模态理解与生成任务。为促进进一步研究，我们呈现了统一模型中模态无关专家路由的详细可视化分析，并结合弹性训练的全面实证研究，旨在为学界提供深刻洞见。

English

In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio. All modalities are trained from scratch under a unified next-group-of-tokens prediction objective, based on an ultra-sparse mixture-of-experts (MoE) architecture with modality-agnostic expert routing. To address practical challenges in large-scale deployment under diverse resource constraints, ERNIE 5.0 adopts a novel elastic training paradigm. Within a single pre-training run, the model learns a family of sub-models with varying depths, expert capacities, and routing sparsity, enabling flexible trade-offs among performance, model size, and inference latency in memory- or time-constrained scenarios. Moreover, we systematically address the challenges of scaling reinforcement learning to unified foundation models, thereby guaranteeing efficient and stable post-training under ultra-sparse MoE architectures and diverse multimodal settings. Extensive experiments demonstrate that ERNIE 5.0 achieves strong and balanced performance across multiple modalities. To the best of our knowledge, among publicly disclosed models, ERNIE 5.0 represents the first production-scale realization of a trillion-parameter unified autoregressive model that supports both multimodal understanding and generation. To facilitate further research, we present detailed visualizations of modality-agnostic expert routing in the unified model, alongside comprehensive empirical analysis of elastic training, aiming to offer profound insights to the community.