Abstract:In Large Language Model (LLM) fine-tuning, parameter and data selection are common strategies for reducing fine-tuning cost, yet they are typically driven by separate scoring mechanisms. When a parameter mask and data subset jointly determine restricted fine-tuning, this separation incurs redundant overhead and makes coordinated selection difficult. We cast parameter and data selection as two bilevel selection problems under a common validation objective and derive a shared local response-surrogate scoring rule. Under first- and second-order validation-improvement approximations, parameter importance and data utility emerge as column-wise and row-wise aggregations of a single gradient interaction matrix, yielding a closed-form row-column correspondence for co-extracting both signals. Building on this structure, we propose DualSFT (Dual-Selection Fine-Tuning), a one-shot dual-scoring algorithm that produces a parameter mask and data subset from shared gradient statistics. On 3B-9B LLMs, single-axis DualSFT variants strengthen target-task performance and stability-plasticity trade-offs within their comparison groups, while full DualSFT yields a more favorable joint-constrained trade-off than sequential hybrid baselines under matched budgets.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.06166 [cs.LG] |
| (or arXiv:2605.06166v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.06166 arXiv-issued DOI via DataCite (pending registration) |
From: Ou Wu [view email]
[v1]
Thu, 7 May 2026 12:52:02 UTC (4,363 KB)
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