一算法同时选参数和数据，提升大模型微调效率。
One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning
- 用统一目标联合优化参数与数据选择，避免分开操作的冗余。
- 在30亿到90亿参数模型上，性能更优且稳定-可塑性平衡更好。
- 适合追求高效微调、资源受限的研究者或工程师使用。
在大语言模型微调中，参数和数据选择常用于降低计算成本，但通常依赖独立的评分机制。当参数掩码与数据子集共同限制微调时，这种分离导致额外开销且难以协同优化。本文将参数与数据选择建模为同一验证目标下的双层选择问题，并推导出共享的局部响应代理评分规则。基于一阶与二阶验证改进近似，参数重要性与数据效用分别表现为单一梯度交互矩阵的列向与行向聚合，从而实现参数与数据信号的闭式行列对应。基于此结构，提出DualSFT（双选微调）算法，通过共享梯度统计量一次性生成参数掩码与数据子集。在30亿至90亿参数模型上，单轴变体显著提升目标任务表现与稳定性-可塑性权衡；全量DualSFT在相同预算下，优于顺序混合基线的联合约束权衡。
原文摘要 · Abstract (English)
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.
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