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arXiv Machine Learning By Xinrui Chen, Liu Yang, Ou Wu

One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning

Read the original on arXiv Machine Learning →

The paper introduces DualSFT, a one-shot algorithm that simultaneously selects a parameter mask and a data subset for fine‑tuning large language models by using a shared gradient‑interaction matrix. By casting both selections as bilevel problems under a common validation objective, the authors derive a unified scoring rule that aggregates parameter importance and data utility as column‑wise and row‑wise sums of the same matrix. Experiments on 3B‑9B LLMs show that DualSFT improves target‑task performance and stability‑plasticity trade‑offs compared to single‑axis variants and outperforms sequential hybrid baselines under matched budgets.

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