arXiv Machine Learning August 26, 2026 By Alexandru-Dragos Manolache, Yunqiang Li, Jan van Gemert

Low-Rank Ternary Adaptation for Fine-Tuning Transformers

Read the original on arXiv Machine Learning →

The paper introduces a ternary multiplicative adaptation technique that enables fine‑tuning of ternary transformers without dequantization. By representing discrete weight updates as a low‑rank Kronecker factorization of two small ternary matrices applied element‑wise, the method preserves the ternary domain and allows direct merging of adaptation weights. Experiments on six language and vision models, including ternarized LLaMA‑3 and ViT‑B/16, show that the approach recovers most of the performance lost to quantization and outperforms existing low‑bit and ternary baselines.

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