arXiv:2602.02855v2 Announce Type: replace Abstract: Pre-training on a source task is usually expected to facilitate fine-tuning on similar downstream problems. In this work, we mathematically show that this naive intuition is not always true: excessive pre-training can computationally slow down fine-tuning optimization. We study this phenomenon for low-rank adaptation (LoRA) fine-tuning on single-index models trained under one-pass SGD. Leveraging a summary statistics description of the fine-tuning dynamics, we precisely characterize how the convergence rate depends on the initial fine-tuning
This paper highlights a critical and previously under-explored computational limitation in widely used AI fine-tuning techniques, moving beyond the simple assumption that more pre-training is always beneficial.
A strategic reader should care because this research challenges fundamental assumptions about AI model development, implying that current practices may be suboptimal and inefficient, impacting resource allocation and training strategies.
The optimal strategy for pre-training and fine-tuning AI models, particularly those using LoRA, is now more nuanced, requiring careful consideration of potential performance degradation rather than monotonic improvement.
- · AI researchers optimizing model training
- · Developers focused on efficient resource use
- · Companies with advanced computational capabilities
- · AI projects over-relying on naive pre-training
- · Organizations with limited compute for extensive experimentation
AI model development pipelines will need to incorporate more sophisticated analysis of pre-training effects on fine-tuning convergence rates.
This could lead to a re-evaluation of 'bigger is better' in pre-training, potentially fostering innovation in more compute-efficient or dynamically adaptive training methodologies.
Long-term, this research may contribute to a shift towards more theoretically grounded and less empirically driven AI development, impacting the overall efficiency and sustainability of AI scaling.
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Read at arXiv cs.LG