Foundation-Preserving Adaptation via Generalized Rayleigh-Quotient Optimization

arXiv:2606.00132v1 Announce Type: new Abstract: While finetuning effectively adapts foundation models to specialized downstream tasks, it can degrade nontarget capabilities acquired during pretraining. Existing forgetting aware methods typically seek safer updates through specialized initialization or fixed constraints, but do not regulate the adaptation preservation trade-off during training. We propose Foundation Preserving LoRA (FoLoRA), a forgetting aware optimization framework. Guided by a first order preservation condition, FoLoRA defines a forgetting penalty over pretraining-proxy activ

Source: arXiv cs.LG — read the full report at the original publisher.

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