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Optical multilayer thin film structures are widely used in many photonic applica- tions, including filters, absorbers, photovoltaics, display devices. The important part to enable these applications is the inverse design, which seeks to identify a suitable structure that satisfy desired optical responses. Recently, a Foundation model-based OptoGPT is proposed and has shown great potential to solve a wide range of inverse design problems. However, OptoGPT fails to design certain types of optical responses that are important to practical applications. The major rea- son is that the training data is randomly sampled and it is highly probable that these design targets are not selected in training, leading to the out-of-distribution issue. In this work, we propose a self-improving data augmentation technique by leveraging neural networks’ extrapolation ability. Using this method, we show sig- nificant improvement in various application design tasks with minimum fine-tuning. The approach can be potentially generalized to other inverse scientific foundation models.