Kronecker Embeddings: Byte-Level Structured Token Representations for Parameter-Efficient Language Models

arXiv:2605.29459v1 Announce Type: cross Abstract: Large language models route every input through a learned embedding table of shape |V| x d_model, consuming hundreds of millions to billions of trainable parameters at frontier scale. We introduce Kronecker Embeddings, a deterministic byte-level character-position factorization that replaces this table with a fixed encoder and a single learned projection, compatible with standard BPE tokenizers, eliminating 91--94% of input-side trainable parameters at frontier scale. We provide five contributions. First, a cross-model probe across six LMs (135

Source: arXiv cs.LG — read the full report at the original publisher.

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