# TernaryLLM preprint reports accuracy gains with lower-bit model weights

_Published Monday, October 12, 2026 at 8:16 AM EDT · AI, Science · Latest · Tier 2 — Notable_

Researchers report in a preprint that TernaryLLM improves language-model benchmark results while representing weights with three values. On LLaMA-3, its W1.58A16 configuration outperforms a previous W2A16 method by 5.8 in C4 perplexity and by 8.2% in average accuracy on zero-shot tasks.

The approach learns both scales and shifts when converting weights, addressing asymmetric outliers and non-zero means. It also transfers information from the floating-point model by matching internal features through cosine similarity. The reported gains concern text-generation and zero-shot benchmarks across different model families; the conversion still requires recovering information lost when weights are reduced to extremely few values.

## Sources

- [cs.LG updates on arXiv.org](https://arxiv.org/abs/2406.07177)

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Canonical: https://techandbusiness.org/newswire/4mGl7SdrcYlljWqPbba-Fk
Published: 2026-10-12T12:16:28.816Z
Story chronology: 2026-10-12T04:00:00.000Z
Retrieved: 2026-10-12T14:11:27.472Z
Publisher: Tech & Business (techandbusiness.org)