  As humans, we navigate a multimodal world, building a holistic understanding
from all our senses. We introduce MERLOT Reserve, a model that represents
videos jointly over time -- through a new training objective that learns from
audio, subtitles, and video frames. Given a video, we replace snippets of text
and audio with a MASK token; the model learns by choosing the correct
masked-out snippet. Our objective learns faster than alternatives, and performs
well at scale: we pretrain on 20 million YouTube videos.
  Empirical results show that MERLOT Reserve learns strong multimodal
representations. When finetuned, it sets state-of-the-art on Visual Commonsense
Reasoning (VCR), TVQA, and Kinetics-600; outperforming prior work by 5%, 7%,
and 1.5% respectively. Ablations show that these tasks benefit from audio
pretraining -- even VCR, a QA task centered around images (without sound).
Moreover, our objective enables out-of-the-box prediction, revealing strong
multimodal commonsense understanding. In a fully zero-shot setting, our model
obtains competitive results on four video tasks, even outperforming supervised
approaches on the recently proposed Situated Reasoning (STAR) benchmark.
  We analyze why audio enables better vision-language representations,
suggesting significant opportunities for future research. We conclude by
discussing ethical and societal implications of multimodal pretraining.
