Not sharing the data is pretty standard because 1) it tends to get the lawyers involved and 2) good data is critical for getting good results.

Imo you can get better results with great data and generic modeling techniques than with incredible modeling techniques and crappy data. Because if you have crappy data, you won’t even know if your model is good because your evals will also be bad.

This is why Anthropic is throwing a fit about the Chinese distillation “attacks”. Clean reasoning traces are gold.

Data has copyright issues, so one can't share it generally without getting permissions from all of the copyright holders. The data is not theirs to share, anyways. The derived (learned) weights are a different matter.

this is very normal for frontier lab companies. you need good data either synthetic or labelled (all the chinese open source models have their own armies of data labelers)