arXiv:2602.11137v2 Announce Type: replace Abstract: Large language models are typically trained in two broad phases: pretraining to produce a base model, followed by further training to improve downstream performance. However, hyperparameter optimization and scaling laws are studied primarily from the perspective of the base model's validation loss, overlooking a crucial model property: downstream adaptability. In this work, we study pretraining from the perspective of model plasticity, that is, the ability of the base model to successfully adapt to downstream tasks upon additional training. W
The continuous growth in size and complexity of large language models necessitates ongoing research into optimizing their training and adaptation processes to improve efficiency and performance.
This research provides a concrete method, weight decay, for enhancing the adaptability of base language models, directly impacting the quality and cost-effectiveness of fine-tuned AI applications.
The understanding of pretraining optimization shifts from solely focusing on base model validation loss to including model plasticity, providing new metrics and approaches for developing more versatile LLMs.
- · AI developers
- · Cloud AI providers
- · Companies deploying fine-tuned LLMs
- · Developers relying on less adaptable base models
- · High-compute, low-plasticity model architectures
Language models become more adaptable to specific downstream tasks with less additional training.
The cost and time required to build specialized AI applications decrease, accelerating AI adoption across industries.
More specialized and performant AI agents emerge, capable of handling complex domain-specific tasks with higher accuracy.
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Read at arXiv cs.LG