It was a useful read for me, esp seeing numbers on fine-tuning & use. We are piloting a DB analyst tool where users can use natural language to do DB queries, generate AI analyses, make interactive GPU charts, etc, so many nearby questions we think about a lot. Previously as a PhD publishing on program synthesis, most of our writeups were much smaller scale wrt live user evals, so all combiner... super cool to see.

For FB... It probably helps keep the team valued internally + helps with retention & recruiting. For PhD trained types, this kind of paper is almost table stakes.

Less obvious... FB has been laying off teams like this despite productivity ROI intuitions, so if I was there, I'd be careful to quantify current + future ROI - I'm sure there are key #'s not being shared.

This paper says: “Customized for the organization: CodeCompose is fine-tuned on Meta’s internal code repository, and is thus able to handle Meta-specific languages such as Hack and Flow.” If you work at an org that might want to build their own LLM trained on their own internal code base, then the lessons of this paper would be of value to you.

There are many scholars working in industry (from software engineering to biotech) who believe in the ideals of information sharing and publication of research.

Of course it does. They give lots of details about the tools and their use which is obviously helpful to anyone wanting to do something similar.

A great example is https://www.uber.com/blog/research/keeping-master-green-at-s...

I think maybe Gitlab Merge Trains predate it, but it was definitely influential.

Google ones seemed to have sparked few revolutions on their own.