对于大型多式联运模型,多式联运学习具有下一个令牌预测
Xinlong Wang1, Yufeng Cui2, Jinsheng Wang2
1Beijing Academy of Artificial Intelligence (BAAI), Beijing, China. xinlong.wang96@gmail.com.
Nature
|January 28, 2026
概括
Emu3是一种新的多式联网模型,用于对文本,图像和视频任务进行下一个令牌预测. 这种统一的方法与没有复杂架构的现有模型相匹配,推进人工智能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 多模式学习,整合文本,图像和视频,是人工智能的关键挑战.
- 目前的方法通常依赖于专门的架构,如扩散模型或组成框架.
- 下一个代币的预测具有先进的语言模型,但其多式联络应用有限.
研究的目的:
- 介绍Emu3,一个新的多式模式模型家族.
- 为了展示一个统一的方法,多式调节学习只使用下一个令牌预测.
- 在各种多式联运任务中实现最先进的性能.
主要方法:
- Emu3模型仅使用下一个令牌预测进行训练.
- 这些模型在多种模式的感知和生成任务上进行了评估.
- 具体应用包括视频生成和视觉语言动作建模.
主要成果:
- Emu3的性能与特定任务模型和旗舰系统相提并论.
- 该模型展示了高保真度视频生成能力.
- Emu3成功执行了交叉视觉语言生成和机器人操纵任务.
结论:
- 通过下一个令牌预测,可以实现统一的多式模式学习.
- Emu3为大规模的多式联络人工智能提供了坚实的基础.
- 这种方法为更普遍和统一的多式联运智能铺平了道路.
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