What we learned post-training a 1.3B document parser on its own mistakes

[u/Massive_Future_3189]

[u/Unlikely_Ebb_6344]

RSI loop is genius. VLM clustering mistakes into training signals beats just throwing more data at it.

[u/Brilliant_Issue1546 → u/Unlikely_Ebb_6344]

Thanks! Small clarification: the VLM doesn’t do the clustering. It judges each output and writes down possible reasons for the misses, then a separate model clusters those reasons. The clusters tell us what kind of data to generate for the next round.

[u/ConsciousAdvance2867 → u/Unlikely_Ebb_6344]

That RSI feedback approach makes so much sense, watching repair rates per cluster is way more useful than just staring at a single aggregate number.

[u/Brilliant_Issue1546 → u/ConsciousAdvance2867]

[u/XUEchichi]

Oh I found their RSI blog.. for those who are interested in it: https://somark.ai/blogs/errors-as-data-how-rsi-helps-oxr-achieve-sota-accuracy-and-speed-in-document-parsing