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All of these smaller model paradigm suggests that we need to incorporate pruning into model training. Neat was one of my favorite algorithms of all time. Same t

All of these smaller model paradigm suggests that we need to incorporate pruning into model training. Neat was one of my favorite algorithms of all time. Same thing with BitNet models which keep showing the information you need is not that much for neural networks. And again, it is same with us, we use much less energy than a regular network so there seems to be immense waste of energy training these models. My intiution tells me the pre-training paradigm will shift immensely in near future because we started to understand that we don’t need all these paramaters since the subnetworks seems to be very robust preserving information in high dimensions. We keep saying curse of dimensionality but it is more like the bliss of dimensionality we keep seeing. Network redundancy still seems to be very high given BitNet is more less comparable to other LLMs. This basically shows over 50% of the neural net is gibberish! The reason being is that the objective function simply does not include it. Again my intiution tells me that neural scaling laws are incomplete as they are because they lack the efficiency parameter that needs to be taken into account (or simply left out due to greed of corporate). And this is what we are seeing as “the wall”. I am no expert in neural network theory nor in math but I would assume the laws should be something in the vicinity of this formulation/simulation: https://colab.research.google.com/drive/1xkTMU2v1I-EHFAjoS86upFb0o8Bw4r6t?usp=sharing https://colab.research.google.com/drive/1xkTMU2v1I-EHFAjoS86... and encapsulate shannon’s channel’s capacity. I call them generalized scaling laws since it includes what it should include in the first place: entropy.

bravura 2y ago
I seem to recall that there a recent theory paper that got a best paper award, but can't find it. If I remember correctly, their counter-intuitive result was that big overparameterized models could learn more efficiently, and were less likely to get trapped in poor regions of the optimization space. [This is also similar to how introducing multimodal training gives an escape hatch to get out of tricky regions.] So with this hand-wavey argument, it might be the case that two-phase training is needed: A large overcomplete pretraining focused on assimilating all the knowledge, and a second that makes it compact. Other, that there is a hyperparameter that controls overcompleteness vs compactness and you adjust it over training.
- furrypony 2y ago
  "Train large, then compress"
- Scene_Cast2 2y ago
  The lottery ticket hypothesis paper from 2018?
  - danielmarkbruce 2y ago
    Seems this way. Gigantic model, hit the jackpot, prune the nonsense. It doesn't seem like smaller models are enough tickets.
- DHRicoF 2y ago
  I don't see that contuer-intuitive at all. If you have a barrier in your cost function in 1d model you have to cross over it no matter what. In 2d it could be only a mount that you can go around. More dimensions mean more ways to go around.
  - fasa99 2y ago
    This is also how the human brain works. A young babby will have something more similar to a fully connected network. Versus a Biden type elderly brain will be more of a sparse minimally connected feed forward net. The question is (1) can this be adjusted dynamically in silico and (2) if we succeed in that, does fine-tuning still work?
    - scotty79 2y ago
      You don't have to compare to old age. Even 10 year old child has its brain pruned immensely when compared to its babyself.
- deleted 2y ago
  [deleted]
- fennecbutt 2y ago
sitkack 2y ago
> This basically shows over 50% of the neural net is gibberish! The reason being is that the objective function simply does not include it. This is a mischaracterization of sparsity. Performance did drop, so the weights are not gibberish. Training vs pruning, you can't train into the final state, you can only prune there.
visarga 2y ago
The fact that you can prune a model will not make it smarter, the wall still stands. I think what explains the wall is the fact that we can't scale organic data exponentially, and we have already covered the most useful types. Going forward we will accumulate truly useful data at a linear growing rate. This fundamentally breaks the scaling game. If your model and compute expand exponentially but your training data only linearly, the efficiency won't be the same. Synthetic data might help us pad up the training sets, but the most promising avenue I think is to use user-LLM chat logs. Those logs contain real world grounding and human in the loop. Millions of humans doing novel tasks. But that only scales linearly with time, as well. No way around it - we only once had the whole internet for the first time in the training set. After that it's linear time.
- mewpmewp2 2y ago
  Don't we still have a lot of video, and other non text real world data to go with? Feels like a possible potential break from there.
  - visarga 2y ago
    Generally speaking text only models manage to learn a huge amount about the visual world. So when you put the model train on video it might have less to learn. Video is also less abstract than text, generally. But I am sure we can still extract useful learning from videos, it's probably expensive, but we'll have to do that at some point.
- griomnib 2y ago
  Given how much of the web is ai generated slop now, I think going forward it’s even worse than you suggest. I have a copy of refined web locally so I have a billion pre-chatgpt documents for my long term use.
nomel 2y ago
In mice, ~30% of neurons are silent [1]. Neuralink team is finding that most are silent, where they probe [2]: > Also, most of them are silent. They don’t really do much. Or their activities are… You have to hit it with just the right set of stimulus. > ... When you place these electrodes, again, within this hundred micron volume, you have 40 or so neurons. Why do you not see 40 neurons? Why do you see only a handful? What is happening there? (Yes, I understand LLM aren't brains.) [1] https://news.mit.edu/2022/silent-synapses-brain-1130 https://news.mit.edu/2022/silent-synapses-brain-1130 [2] https://youtube.com/watch?v=Kbk9BiPhm7o&t=7056 https://youtube.com/watch?v=Kbk9BiPhm7o&t=7056

bravura 2y ago

I seem to recall that there a recent theory paper that got a best paper award, but can't find it. If I remember correctly, their counter-intuitive result was that big overparameterized models could learn more efficiently, and were less likely to get trapped in poor regions of the optimization space. [This is also similar to how introducing multimodal training gives an escape hatch to get out of tricky regions.] So with this hand-wavey argument, it might be the case that two-phase training is needed: A large overcomplete pretraining focused on assimilating all the knowledge, and a second that makes it compact. Other, that there is a hyperparameter that controls overcompleteness vs compactness and you adjust it over training.

furrypony 2y ago
"Train large, then compress"
Scene_Cast2 2y ago
The lottery ticket hypothesis paper from 2018?
- danielmarkbruce 2y ago
  Seems this way. Gigantic model, hit the jackpot, prune the nonsense. It doesn't seem like smaller models are enough tickets.
DHRicoF 2y ago
I don't see that contuer-intuitive at all. If you have a barrier in your cost function in 1d model you have to cross over it no matter what. In 2d it could be only a mount that you can go around. More dimensions mean more ways to go around.
- fasa99 2y ago
  This is also how the human brain works. A young babby will have something more similar to a fully connected network. Versus a Biden type elderly brain will be more of a sparse minimally connected feed forward net. The question is (1) can this be adjusted dynamically in silico and (2) if we succeed in that, does fine-tuning still work?
  - scotty79 2y ago
    You don't have to compare to old age. Even 10 year old child has its brain pruned immensely when compared to its babyself.
deleted 2y ago
[deleted]
fennecbutt 2y ago

sitkack 2y ago

> This basically shows over 50% of the neural net is gibberish! The reason being is that the objective function simply does not include it. This is a mischaracterization of sparsity. Performance did drop, so the weights are not gibberish. Training vs pruning, you can't train into the final state, you can only prune there.

visarga 2y ago

The fact that you can prune a model will not make it smarter, the wall still stands. I think what explains the wall is the fact that we can't scale organic data exponentially, and we have already covered the most useful types. Going forward we will accumulate truly useful data at a linear growing rate. This fundamentally breaks the scaling game. If your model and compute expand exponentially but your training data only linearly, the efficiency won't be the same. Synthetic data might help us pad up the training sets, but the most promising avenue I think is to use user-LLM chat logs. Those logs contain real world grounding and human in the loop. Millions of humans doing novel tasks. But that only scales linearly with time, as well. No way around it - we only once had the whole internet for the first time in the training set. After that it's linear time.

mewpmewp2 2y ago
Don't we still have a lot of video, and other non text real world data to go with? Feels like a possible potential break from there.
- visarga 2y ago
  Generally speaking text only models manage to learn a huge amount about the visual world. So when you put the model train on video it might have less to learn. Video is also less abstract than text, generally. But I am sure we can still extract useful learning from videos, it's probably expensive, but we'll have to do that at some point.
griomnib 2y ago
Given how much of the web is ai generated slop now, I think going forward it’s even worse than you suggest. I have a copy of refined web locally so I have a billion pre-chatgpt documents for my long term use.

nomel 2y ago

In mice, ~30% of neurons are silent [1]. Neuralink team is finding that most are silent, where they probe [2]: > Also, most of them are silent. They don’t really do much. Or their activities are… You have to hit it with just the right set of stimulus. > ... When you place these electrodes, again, within this hundred micron volume, you have 40 or so neurons. Why do you not see 40 neurons? Why do you see only a handful? What is happening there? (Yes, I understand LLM aren't brains.) [1] https://news.mit.edu/2022/silent-synapses-brain-1130 https://news.mit.edu/2022/silent-synapses-brain-1130 [2] https://youtube.com/watch?v=Kbk9BiPhm7o&t=7056 https://youtube.com/watch?v=Kbk9BiPhm7o&t=7056