语言大模型 / LLM

Fast LLM Post-training via Decoupled and Fastest-of-N Speculation

快速LLM后训练通过解耦和最快-N推测

Rongxin Cheng, Kai Zhou, Xingda Wei, Siyuan Liu, Mingcong Han, Mingjing Ai, Yeju Zhou, Baoquan Zhong, Wencong Xiao, Rong Chen, Haibo Chen

机构 * Institute of Parallel and Distributed Systems, Shanghai Jiao Tong University（并行与分布式系统研究所，上海交通大学） ； ByteDance Seed（字节跳动种子） 纠错

专题命中 后训练与偏好优化 ：LLM(title,abstract)；post-training(title,abstract)；large language model(abstract)；language model(abstract)

AI总结 SpecActor通过解耦和最快-N推测方法提升LLM后训练效率，实现2.0-2.7倍的rollout加速和1.4-2.3倍的端到端训练时间提升。

Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model

Seedance 1.5 pro: 一种原生音频视频联合生成基础模型

Team Seedance, Heyi Chen, Siyan Chen, Xin Chen, Yanfei Chen, Ying Chen, Zhuo Chen, Feng Cheng, Tianheng Cheng, Xinqi Cheng, Xuyan Chi, Jian Cong, Jing Cui, Qinpeng Cui, Qide Dong, Junliang Fan, Jing Fang, Zetao Fang, Chengjian Feng, Han Feng, Mingyuan Gao, Yu Gao, Dong Guo, Qiushan Guo, Boyang Hao, Qingkai Hao, Bibo He, Qian He, Tuyen Hoang, Ruoqing Hu, Xi Hu, Weilin Huang, Zhaoyang Huang, Zhongyi Huang, Donglei Ji, Siqi Jiang, Wei Jiang, Yunpu Jiang, Zhuo Jiang, Ashley Kim, Jianan Kong, Zhichao Lai, Shanshan Lao, Yichong Leng, Ai Li, Feiya Li, Gen Li, Huixia Li, JiaShi Li, Liang Li, Ming Li, Shanshan Li, Tao Li, Xian Li, Xiaojie Li, Xiaoyang Li, Xingxing Li, Yameng Li, Yifu Li, Yiying Li, Chao Liang, Han Liang, Jianzhong Liang, Ying Liang, Zhiqiang Liang, Wang Liao, Yalin Liao, Heng Lin, Kengyu Lin, Shanchuan Lin, Xi Lin, Zhijie Lin, Feng Ling, Fangfang Liu, Gaohong Liu, Jiawei Liu, Jie Liu, Jihao Liu, Shouda Liu, Shu Liu, Sichao Liu, Songwei Liu, Xin Liu, Xue Liu, Yibo Liu, Zikun Liu, Zuxi Liu, Junlin Lyu, Lecheng Lyu, Qian Lyu, Han Mu, Xiaonan Nie, Jingzhe Ning, Xitong Pan, Yanghua Peng, Lianke Qin, Xueqiong Qu, Yuxi Ren, Kai Shen, Guang Shi, Lei Shi, Yan Song, Yinglong Song, Fan Sun, Li Sun, Renfei Sun, Yan Sun, Zeyu Sun, Wenjing Tang, Yaxue Tang, Zirui Tao, Feng Wang, Furui Wang, Jinran Wang, Junkai Wang, Ke Wang, Kexin Wang, Qingyi Wang, Rui Wang, Sen Wang, Shuai Wang, Tingru Wang, Weichen Wang, Xin Wang, Yanhui Wang, Yue Wang, Yuping Wang, Yuxuan Wang, Ziyu Wang, Guoqiang Wei, Wanru Wei, Di Wu, Guohong Wu, Hanjie Wu, Jian Wu, Jie Wu, Ruolan Wu, Xinglong Wu, Yonghui Wu, Ruiqi Xia, Liang Xiang, Fei Xiao, XueFeng Xiao, Pan Xie, Shuangyi Xie, Shuang Xu, Jinlan Xue, Shen Yan, Bangbang Yang, Ceyuan Yang, Jiaqi Yang, Runkai Yang, Tao Yang, Yang Yang, Yihang Yang, ZhiXian Yang, Ziyan Yang, Songting Yao, Yifan Yao, Zilyu Ye, Bowen Yu, Jian Yu, Chujie Yuan, Linxiao Yuan, Sichun Zeng, Weihong Zeng, Xuejiao Zeng, Yan Zeng, Chuntao Zhang, Heng Zhang, Jingjie Zhang, Kuo Zhang, Liang Zhang, Liying Zhang, Manlin Zhang, Ting Zhang, Weida Zhang, Xiaohe Zhang, Xinyan Zhang, Yan Zhang, Yuan Zhang, Zixiang Zhang, Fengxuan Zhao, Huating Zhao, Yang Zhao, Hao Zheng, Jianbin Zheng, Xiaozheng Zheng, Yangyang Zheng, Yijie Zheng, Jiexin Zhou, Jiahui Zhu, Kuan Zhu, Shenhan Zhu, Wenjia Zhu, Benhui Zou, Feilong Zuo

专题命中 后训练与偏好优化 ：foundation model(title)；post-training(abstract)；SFT(abstract)；RLHF(abstract)

AI总结 Seedance 1.5 pro是一款专为原生音频视频联合生成设计的基础模型，通过双分支扩散变压器架构实现高质量生成，支持多语言唇形同步和动态摄像机控制，提升专业内容创作效率。

Comments Seedance 1.5 pro Technical Report

CoPHo: Classifier-guided Conditional Topology Generation with Persistent Homology

CoPHo：基于持续同调的分类器引导条件拓扑生成

Gongli Xi, Ye Tian, Mengyu Yang, Zhenyu Zhao, Yuchao Zhang, Xiangyang Gong, Xirong Que, Wendong Wang

机构 * School of Cyberspace Security, Beijing University of Posts and Telecommunications（网络安全学院，北京邮电大学） ； State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（网络与交换技术国家重点实验室，北京邮电大学） ； School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications（计算机学院（国家级试点软件工程学院），北京邮电大学） 纠错

专题命中 后训练与偏好优化 ：post-training(abstract)；分类 cs.AI、cs.LG

AI总结 CoPHo通过持续同调和预训练分类器引导生成具有特定结构属性的拓扑图，优于现有方法并在分子数据集上验证了其可迁移性。

Comments Accepted by KDD 2026. 12 pages, 5 figures

Offline Safe Policy Optimization From Heterogeneous Feedback

基于异质反馈的离线安全策略优化

Ze Gong, Pradeep Varakantham, Akshat Kumar

机构 * Shenzhen Institute of Advanced Technology (SIAT), CAS（深圳先进技术研究院（SIAT）、中国科学院） ； Singapore Management University（新加坡管理学院） 纠错

专题命中 后训练与偏好优化 ：RLHF(abstract)；分类 cs.AI

AI总结 本文提出PreSa方法，通过直接学习基于偏好和安全性的策略，解决离线安全强化学习中的性能下降问题。

Comments Accepted at AAMAS 2026 (Extended Abstract)