DualSFT derives parameter masks and data subsets as row- and column-wise aggregations of one gradient interaction matrix under first- and second-order validation-improvement approximations.
Parameter importance-driven continual learning for foundation mod- els
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One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning
DualSFT derives parameter masks and data subsets as row- and column-wise aggregations of one gradient interaction matrix under first- and second-order validation-improvement approximations.