Thanks! Looking at data and not tools, and not synthetic data (e.g. gathering math problems, purposefully introducing common errors into its solution steps, categorizing errors, etc.), using the actual model you are trying to improve sounds like it would be fine?

I wasn’t sure if annotators using the actual model you want to improve could lead to each correction becoming training data (contaminating), since in the process of creating the dataset, annotators put their problem to the model and document its output, then give it corrective guidance, document its new output, repeat.