Fixed Minimal Token Codes Enable LLM Training Without Learned Embeddings
October 1, 2026
Research shows that 1.7B-class scale models can achieve substantial capabilities using fixed 16-bit token-ID codes or invertible recoding instead of trainable input embedding tables. The study compares these fixed-code models against standard learned tables over 100 billion prediction tokens.
HOW THIS AFFECTS YOU
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builderYou may be able to reduce parameter counts and training complexity by using fixed token-ID representations.
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researcherThis challenges the necessity of trainable embedding tables for large-scale language modeling.