Freeze Deep, Train Shallow: Interpretable Layer Allocation for Continued Pre-Training
Selective layer-wise updates are essential for low-cost continued pre-training of Large Language Models (LLMs), yet determining which layers to freeze or train remains an empirical black-box problem due to the lack of interpretable guidance. To address this issue, we propose LayerTracer, an architecture-agnostic diagnostic framework that reveals the evolution patterns of layer-wise representations and stability by locating task execution positions and quantifying layer sensitivity. Analysis results reveal that deep layers act as critical regions for task execution and maintain high stability against disruptive updates. Guided by this finding, we conduct three controlled continued pre-trai...
| Authors | Yu-Hang Wu, Qin-Yuan Liu, Qiu-Yang Zhao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 May 2026 |
| Last updated at source | 21 May 2026 |
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