Wasn’t it the case that for some reason, full distillation had comparable compute requirement to data filtering? I was surprised by that. My impression is that distillation should be more like 10% of pretraining (data filtering), which would make the computational UNDO results much stronger. Not sure what happened here.
Yeah, I’m also surprised by it. I have two hypotheses, but it could be for other reasons I’m missing. One hypothesis is that we kept temperature=1 for the KL divergence, and using a different temperature might be important to distill faster. The second is that we undertrained the pretrained models, so pretraining was shorter while distillation took around the same amount of time. I’m not really sure though.
Wasn’t it the case that for some reason, full distillation had comparable compute requirement to data filtering? I was surprised by that. My impression is that distillation should be more like 10% of pretraining (data filtering), which would make the computational UNDO results much stronger. Not sure what happened here.
Yeah, I’m also surprised by it. I have two hypotheses, but it could be for other reasons I’m missing. One hypothesis is that we kept temperature=1 for the KL divergence, and using a different temperature might be important to distill faster. The second is that we undertrained the pretrained models, so pretraining was shorter while distillation took around the same amount of time. I’m not really sure though.