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| ▲ | dTal 5 hours ago | parent | next [-] |
| None of that is true, at least in theory. You can trivially change layer size simply by adding extra columns initialized as 0, effectively embedding your smaller network in a larger network. You can add layers in a similar way, and in fact LLMs are surprisingly robust to having layers added and removed - you can sometimes actually improve performance simply by duplicating some middle layers[0]. Tokenization is probably the hardest but all the layers between the first and last just encode embeddings; it's probably not impossible to retrain those while preserving the middle parts. [0]
https://news.ycombinator.com/item?id=47431671
https://news.ycombinator.com/item?id=47322887 |
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| ▲ | thesz 3 hours ago | parent | next [-] | | You took a simple path, embedding smaller into larger. What if you need to reduce number of layers and/or width of hidden layers? How will you embed larger into smaller? As for the "addition of same layers" - would the process of "layers to add" selection be considered training? What if you still have to obtain the best result possible for given coefficient/tokenization budget? I think that my comment express general case, while yours provide some exceptions. | |
| ▲ | andriy_koval 3 hours ago | parent | prev [-] | | there is evidence it is useful in some cases, but obviously no evidence it is enough if you chase to beat SOTA. |
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| ▲ | altruios 5 hours ago | parent | prev | next [-] |
| Hopefully we will find a way to make it so that making minor changes don't require a full retrain. Training how to train, as a concept, comes to mind. |
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| ▲ | CamperBob2 5 hours ago | parent | prev [-] |
| And yet the KL divergence after changing all that stuff remains remarkably similar between different models, regardless of the specific hyperparameters and block diagrams employed at pretraining time. Some choices are better, some worse, but they all succeed at the game of next-token prediction to a similar extent. To me, that suggests that transformer pretraining creates some underlying structure or geometry that hasn't yet been fully appreciated, and that may be more reusable than people think. Ultimately, I also doubt that the model weights are going to turn out to be all that important. Not compared to the toolchains as a whole. |
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| ▲ | thesz 3 hours ago | parent [-] | | That "underappreciated underlying structure or geometry" can be just an artifact of the same tokenization used with different models. Tokenization breaks up collocations and creates new ones that are not always present in the original text as it was. Most probably, the first byte pair found by simple byte pair encoding algorithm in enwik9 will be two spaces next to each other. Is this a true collocation? BPE thinks so. Humans may disagree. What does concern me here is that it is very hard to ablate tokenization artifacts. |
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