arXiv:2605.11416v2 Announce Type: replace Abstract: 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 regi
The rapid scaling of Large Language Models (LLMs) requires more efficient continued pre-training methods to manage computational costs and accelerate development, making layer-wise optimization crucial.
This research provides a diagnostic framework that can significantly reduce the cost and improve the efficiency of adapting LLMs for new tasks, directly impacting AI development cycles and resource allocation.
The empirical 'black-box' problem of determining which LLM layers to train or freeze is being replaced by an interpretable, diagnostic approach, enabling more strategic and less resource-intensive model fine-tuning.
- · AI developers
- · Cloud computing providers
- · Companies deploying LLMs
- · Researcher (cs.CL)
- · Companies with inefficient LLM fine-tuning processes
- · Hardware providers whose product differentiation relied solely on raw compute fo
More efficient and cost-effective continued pre-training of large language models.
Accelerated deployment of specialized LLMs and a reduction in the carbon footprint of AI development.
Lower barriers to entry for developing and adapting advanced AI, leading to broader accessibility and potentially more diverse AI applications.
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Read at arXiv cs.CL