A study at 1.7B-class scale found that a shared Transformer can learn language modeling from fixed token identities rather than a trainable input embedding table, according to the paper's headline claim. The research compares fixed minimal token codes against token-specific adjustable vectors to test whether per-token parameterization is required for substantial capability.
A trainable input embedding table assigns each vocabulary item an independently adjustable vector. We investigate whether this token-specific parameterization is required for substantial language-modeling capability, or whether a shared Transformer can learn from fixed token identities. We compare t
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