It might be that our current tokenization is inefficient compared to how well image pipeline does. Language already does lot of compression but there might be even better way to represent it in latent space
replies(3):
Removing the tokenizer would 1/4 the context and 4x the compute and memory, assuming an avg token length of 4.
Also, you would probably need to 4x the parameters to have to learn understanding between individual characters as well as words and sentences etc.
There's been a few studies on small models, even then those only show a tiny percentage gain over tokenized models.
So essentially you would need 4x compute, 1/4 the context, and 4x the parameters to squeeze 2-4% more performance out of it.
And that fails when you use more then 1/4 context. So realistically you need to support the same context, so you r compute goes up another 4x to 16x.
That's why