I've recently come across a paper that uses the term "degenerate run", but I'm not sure if I understand what it means. The idea is that when they report the average performance of running fine-tuned models using multiple random seeds (e.g., a deep learning model where we need to initialize model parameters using multiple seeds to ensure results are robust,) they exclude the degenerate runs in some of their analyses.
As this paper mentions, a degenerate run is "where fine-tuned models fail to outperform the random baseline." But I wonder if this is a standard practice to eliminate such results when reporting the average performance? Or is the definition they give the correct meaning of a degenerate run?