distillation

“For example, all this proprietary stuff sucks at clocks, so nothing that's a distillation will be good at clocks. Nothing can point if you just distill from this. If they can't point, your VLM won't point, so we show how to get good data.”

“The distillation doesn't do quite as well as the training. And, but neither of them do as well as the diffusion teacher. Including the one that was trained from scratch.”

“So, so I would think maybe an optimal pipeline would look like you do distillation heavily, but then you still do some RL afterwards, because maybe there's still something you can get out of your reward functions or whatever your post-training stack is.”

“First step is you go to an RLHF, RLXF, whatever framework you have. You turn off the reward term. So you delete the reward part of it. All you are left is some KL term. And now you swap your KL, the anchor policy, to a bigger teacher policy. And there you go. …”

“GemRTool used distillation for pre-training, where they used logits, or these soft labels, which is rather than having hard zero, one tokens, which is, I want to predict this next token, they have like soft labels for all possible tokens.”

“The slope of the Pareto Frontier is basically the state of the art of distillation, and the gentler the slope, the better distillation is.”

“The general recommendation I would give people is that maybe use a mixture of half and half. Like that's what some people have used, right? That's like saying, yeah, basically saying, I don't know what I want. I just want something to work well enough. I'll ju…”

“There's a trade-off between diversity and performance. So on the y-axis, I'm showing performance. On the x-axis, I'm showing similarity or basically how, like one minus diversity. So more similar things are less diverse. And you can see, depending on the diver…”

“The capability which we have right now, maybe next year will be much cheaper to have that same thing. And that likely is the result of distillation, right? Like that's, it's not just because we are doing or figured out something magical. It is because distilla…”

“You can actually combine RLHF and distillation together, because now you're doing two things at the same time.”

“Now, the thing is, the effectiveness of this method depends on how close the sampler, the small model is to the bigger model that we want to speed up, and actually distillation exactly fixes that, which is, by distillation, you can make things closer to each o…”

“Actually, distillation is like quite a big thing that people in the open are not really Really doing yet, I think”

“One of the key advantages of distillation is that you can have a much smaller model And you can have a very large you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the mu…”

“Through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order t…”