Details of architectural search

I noticed that in your paper, you mentioned conducting a quick but reasonable architectural search using Video-LLaVA's training data. I'm interested in performing a similar search, but I noticed that the scale of Video-LLaVA's training data is quite large.

Could you please elaborate on the important details of the training, such as whether you used the entire dataset or a subset, and how many epochs you trained for?

No files, tests, or entry points are identified. Start by reviewing the paper and the issue's reference to Video-LLaVA's training data, then determine whether the full dataset or a subset was used and how many epochs were run. Done means those architectural-search training details are documented clearly.

Written by the indexing model from the issue text.