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arXiv Machine Learning By Tung-Ling Li, Jiale Huang, Lee-Chi Wang, Janaki Ram Gotei

Scale-QLoRA: Code-Invariant Adapter Merging for Native 4-bit Microscaling LLMs

Read the original on arXiv Machine Learning →

Scale-QLoRA introduces a method for merging LoRA adapters into native 4‑bit microscaling language models without altering the quantized code plane. By training only the per‑block scale field and freezing the E2M1 code, the merged model remains code‑invariant and can be deployed as a single checkpoint. Experiments on four models and tasks show that Scale‑QLoRA achieves accuracy‑lossless merging comparable to merge‑aware QAT‑LoRA, while offering benefits such as exact rollback, code‑plane deduplication, and a 125× faster scale‑only task swap.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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