Weird paper. Models have had tokens for each byte for ages now. They can read and write individual bytes just fine, in addition to also having multibyte tokens.
Wow nature got rolled. Should have stayed closer to their expertise. They were already being hustled by a lot of the applied AI work they were accepting.
Obvious work that is behind state of the art. Not field defining. A reasonable paper to publish at NeurIPS or ICML but very middle of the pack.
If your paper is accepted to Nature it should be among the top results in your field for the year. This is just fine.
Edit to clarify: Nature has not been, historically, a venue for pure machine learning papers. It's been a venue for field changing work in the physical sciences. They already have a Machine Intelligence subjournal.
What this paper shows me is how desperate they are for ML papers, and how poorly their staff understand the field.
I think nature sort of has a reputation for sensationalism / probably overhyped (or outright wrong) stuff in the sciences now anyway... would not surprise me at all that they're desperate for ml stuff :-I
Byte Latent Transformers (BLT): https://arxiv.org/abs/2412.09871
Charformer: https://arxiv.org/abs/2106.12672
If your paper is accepted to Nature it should be among the top results in your field for the year. This is just fine.
Edit to clarify: Nature has not been, historically, a venue for pure machine learning papers. It's been a venue for field changing work in the physical sciences. They already have a Machine Intelligence subjournal.
What this paper shows me is how desperate they are for ML papers, and how poorly their staff understand the field.