arXiv AI October 9, 2026 By Zhiqiang Pang, Zihong Sun, Qi Xie, Jun Shu, Deyu Meng, Zongben Xu

Where to Adapt Matters: Layer-Selective Fine-Tuning for Capability Retention

The paper investigates how the choice of Transformer layers for fine‑tuning affects both task performance and the preservation of pre‑trained capabilities. By measuring layer sensitivity with a forward‑only input–output cosine similarity metric, the authors identify layers that are more suitable for adaptation. They introduce Layer‑Selective LoRA (LS‑LoRA), which trains adapters only in low‑similarity layers, and demonstrate that this approach improves target‑task results while better retaining commonsense reasoning compared to standard all‑layer LoRA.

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