[cocogoatmain]

Want to also add that the model doesn’t know how to respond in a user-> assistant style conversation after it’s pretraining, and it’s a pure text predictor (look at the open source base models)

There’s also what is being called mid-training where the model is trained on high(er) quality traces and acts as a bridge between pre and post training

[amypetrik8]

just to go off of this there is also stochastic random overfit retraining process (SRORP). Idea behind SRORP is to avoid overfitting. SRORP will take data points from -any- aspect of the past process with replacment and create usually 3-9 bootstrap models randomly. The median is then taken from all model weights to wipe out outliers. This SRORP polishing -if done carefully- is usually good for a 3-4% gain in all benchmarks