A new tokenizer ships fresh dead zones, and every model now carries a graveyard of glitch tokens nobody has mapped yet.
Why does the system allow meaning to collapse while remaining structurally valid?
because the tokenizer and the training loop are two different systems that never talk to each other, essentially.
the tokenizer gets built from one corpus, picking merges based on frequency.
the embeddings get updated from a different corpus, based on gradient flow.
a slot can exist in the vocabulary and never receive a single gradient update. the vector stays at initialization noise forever.
at runtime, the forward pass doesn't care. token ID lookup succeeds.
the model just happens to be reasoning over a vector that means nothing. garbage in, fluent-sounding garbage out.
and we get strange behaviors! sometimes 1 word jailbreaks =)
Why does the system allow meaning to collapse while remaining structurally valid?
because the tokenizer and the training loop are two different systems that never talk to each other, essentially.
the tokenizer gets built from one corpus, picking merges based on frequency.
the embeddings get updated from a different corpus, based on gradient flow.
a slot can exist in the vocabulary and never receive a single gradient update. the vector stays at initialization noise forever.
at runtime, the forward pass doesn't care. token ID lookup succeeds.
the model just happens to be reasoning over a vector that means nothing. garbage in, fluent-sounding garbage out.
and we get strange behaviors! sometimes 1 word jailbreaks =)