Our key finding is that by injecting information through an external synthetic data verifier, whether a human or a better model, synthetic retraining will not cause model collapse.
Lol, so to make a great model, they just need to have an even better one available first or a human who can verify every single thing it ingests.
Our key finding is that by injecting information through an external synthetic data verifier, whether a human or a better model, synthetic retraining will not cause model collapse.
Yeah if you have a source of truth then your model is basically getting trained on that.
My point was that having a verifier means your not really training a model on another model’s data, it’s basically as if you get new raw data from a non AI source
This assumes everything is valid on the external. If one slop cluster feeds off another - a slopveyor? - then there is nothing external for the validation hall-monitor to compare against. They’re trusting another model’s output as if it were gospel.
Model collapse isn’t a thing anymore. https://arxiv.org/html/2510.16657v1
Lol, so to make a great model, they just need to have an even better one available first or a human who can verify every single thing it ingests.
Hmm, call me skeptical on this claim.
Yeah if you have a source of truth then your model is basically getting trained on that.
It’s like already having the answer
The point is that it only needs to comprise a very small part of the model.
My point was that having a verifier means your not really training a model on another model’s data, it’s basically as if you get new raw data from a non AI source
This assumes everything is valid on the external. If one slop cluster feeds off another - a slopveyor? - then there is nothing external for the validation hall-monitor to compare against. They’re trusting another model’s output as if it were gospel.
LOL OK