On the Robotics team at OpenAI, we have heavily adopted W&B Reports into our workflow and over the last ~6 months have shifted to using them as our primary means of sharing results within the team. The ability to mix real data from experiments with context and commentary on the results was the primary selling point for us; prior to Reports, we typically would create a Google Doc for tracking all of the "runs" in a given line of experimentation, and would have to spend a fair amount of time properly linking these docs to the experimental data (either in W&B or Tensorboard). Reports save us from this tedious bookkeeping while also allowing us to more easily share complete views on the data (since the viewer can select which runs to view, drill into specific runs, or even clone the report to add more plots).
Whenever we begin a new line of experimentation (e.g. batch size ablations, architecture search), we tend to use the following workflow with Reports:
The rest of this Report presents one example of this workflow applied to the line of research aimed at solving the block reorientation task from our Learning Dexterity release in an end-to-end manner (there is of course a bit more context included here compared to internal reports). If you are unfamiliar with this work, I suggest reading through the collapsed "Background" section below.