arXiv:2606.16243v1 Announce Type: cross Abstract: This paper proposes a Linear Programming (LP)-based local search framework for fine-tuning pretrained transformer models with explicit control against overfitting. The approach formulates transformer fine-tuning as a bilevel optimization-based regularization problem, in which model parameters and regularization hyperparameters are jointly updated. Information collected during initial warm-up iterations, including validation gradients and training Hessian information, is used to construct a local descent direction by solving an LP that minimizes
The continuous evolution of transformer models highlights an ongoing need for more robust fine-tuning methods, particularly those addressing overfitting as models scale and become more complex.
This research introduces a novel technique that promises to make advanced AI models more reliable and efficient by better controlling overfitting during fine-tuning, which is crucial for deployment in critical applications.
The ability to formally control overfitting during fine-tuning could lead to more stable and trustworthy AI models, potentially reducing the need for extensive manual hyperparameter tuning and improving model generalizability.
- · AI developers
- · Companies deploying AI models
- · Transformer language model users
- · Developers relying on ad-hoc overfitting solutions
Transformer models become more robust and deployable in production environments due to explicit overfitting control.
Reduced computational costs and time for fine-tuning as the process becomes more optimized and less prone to iterative trial-and-error.
Broader adoption of AI in sensitive applications where model stability and reliability are paramount, accelerating automation across various sectors.
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Read at arXiv cs.CL