this original technique is used for identifying bad behaviors which come from DPO preference data - it can be extended quite trivially to analyze failures on eval benchmarks (if you have a ground truth demonstration that succeeds). I've found it extremely useful at clustering failure modes, e.g. identifying only one instance in a segmentation problem, or outputting many degenerate boxes in a sort of doom loop. im excited to extend this to filtering SFT data. this was inspired by an interesting convo with @SwannAiden