There are so many possible naturalistic encodings. Waveforms, one-hot encoded discrete articulatory features, motor programs for simulated vocal apparatus, IPA, bitmaps of printed characters (handwritten? averaged across a bunch of fonts?), SVG programs to draw characters, decomposing big CJK chars via Ideographic Description Sequence, treating all unicode codepoints equally (kinda halfway between bytes and tokens)...
I don't think a thorough survey exists.
[–] jaen 13 hours ago
[–] jephs 12 hours ago
[–] jadermcs 9 hours ago
[–] vatsachak 16 hours ago
[–] jephs 12 hours ago
Vocalizations, hand writing, and keys strokes are raw data.
Bytes are downstream of both those raw communicative performances, and of arbitrary encoding schemes, and of several further layers abstraction. There are byte sequences representing Unicode codepoints that will display different glyphs depending on whether the user's OS and browser are configured like a typical person from mainland China, Taiwan, or Japan.
A single Latin letter is one byte. A single Cyrillic letter that looks identical displayed on the screen and indicates the same underlying phonological value is two bytes. That is not raw.
The article's approach just requires some indivisible base "atoms" to start the building the larger "tokens" from. (see: the DNA example)
It could be raw bits, but that's pointless, since dividing bytes into bits does not give the model any extra expressive power and requires 8× more computation. (yes, there's some structure at the bit level eg. lowercase/uppercase is represented as a bit in ASCII, but that's trivial to learn from just bytes at the scale the model operates)
Bytes are the ~best point on the expressivity/computation curve for this approach (for text).
(something else to ponder - why do bytes have 8 bits?)