Current language models fall short in understanding aspects of the world not
easily described in words, and struggle with complex, long-form tasks. Video
sequences offer valuable temporal information absent in language and static
images, making them attractive for joint modeling with language. Such models
could develop a understanding of both human textual knowledge and the physical
world, enabling broader AI capabilities for assisting humans. However, learning
from millions of tokens of video and language sequences poses challenges due to
memory constraints, computational complexity, and limited datasets. To address
these challenges, we curate a large dataset of diverse videos and books,
utilize the RingAttention technique to scalably train on long sequences, and
gradually increase context size from 4K to 1M tokens. This paper makes the
following contributions: (a) Largest context size neural network: We train one
of the largest context size transformers on long video and language sequences,
setting new benchmarks in difficult retrieval tasks and long video
understanding. (b) Solutions for overcoming vision-language training
challenges, including using masked sequence packing for mixing different
sequence lengths, loss weighting to balance language and vision, and
model-generated QA dataset for long sequence chat. (c) A highly-optimized
implementation with RingAttention, masked sequence packing, and other key
features for training on millions-length multimodal sequences. (d) Fully
open-sourced a family of 7B parameter models capable of processing long text
documents (LWM-Text, LWM-Text-Chat) and videos (LWM, LWM-Chat) of over 1M
tokens. This work paves the way for training on massive datasets of long video
and language to develop understanding of both human knowledge and the
multimodal world, and broader capabilities.

当前语言模型在理解不易用文字描述的世界方面存在不足，并且在处理复杂和长期任务时存在困难。该论文通过利用视频序列的时间信息和语言的静态图像之间的联合建模，在人类文本知识和物理世界方面形成了深入的理解，从而实现了更广泛的人工智能能力。