# Researchers test fixed token codes for language models at 100B-token scale

Digest AI · Research · published 2026-10-06T04:00:00Z

Canonical: https://digestai.news/story/researchers-test-fixed-token-codes-for-language-models-at-100b-token-s

## Summary

A new paper on arXiv explores whether language models need trainable input embeddings to function well. The authors trained three decoder-only models from scratch with identical tokenizers, backbones, and training recipes, each using a different input interface: a learned embedding table, canonical 16-bit token-ID codes, and a fixed invertible recoding over GF(2). All models were trained on a target budget of **100 billion prediction tokens** each.

The fixed-code models achieved strong performance: **52.40%** on HellaSwag, **70.51%** on PIQA, and **42.75%** on LAMBADA. While the learned-input model outperformed the fixed ones on some benchmarks, the results suggest fixed token codes can be viable without sacrificing capability. The fixed interfaces also reduced trainable parameters by **100.7 million**, yielding models with **1.7B parameters**—though the paper emphasizes this is not the main finding. The study aims to clarify whether token-specific input vectors are architecturally necessary or empirically useful.

## Key points

- Three models trained on 100B tokens each, using learned embeddings, fixed 16-bit codes, and GF(2) recoding
- Fixed-code models scored 52.40% on HellaSwag, 70.51% on PIQA, and 42.75% on LAMBADA
- Learned embeddings outperformed fixed codes but proved they are not strictly required for model capability

## Why it matters

If fixed token codes work as well as learned embeddings, it could simplify model architecture and reduce training costs without losing performance.

## Sources

1. [Do Language Models Need a Trainable Input Embedding Table? Fixed Minimal Token Codes at 1.7B-Class Scale](https://arxiv.org/abs/2610.04002) (arXiv cs.CL, 2026-10-06, primary source)

## Cite

Digest AI, "Researchers test fixed token codes for language models at 100B-token scale", 6 October 2026, https://digestai.news/story/researchers-test-fixed-token-codes-for-language-models-at-100b-token-s

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