Data selection for LLM fine-tuning faces two facts about real training: data pools draw from many distinct domains, and evaluation spans diverse tasks.
As a result, the same sample helps different tasks differently, and samples interact during training so that a subset's value is not the sum of its parts.
Current data-selection methods leave these realities largely unaddressed.
We formalize them as Combinatorial Data Selection (CDS), a framework that separates attribution (how samples are scored) from selection (which subset to pick) and models the non-additive interactions among samples.
On a real instruction-tuning data pool, we find substantial cross-domain complementarity (joint utility exceeds the sum of singletons), alongside redundancy within domains.
Inspired by this, we propose Mixture-then-Select, a two-stage algorithm that first decides how many samples to take from each domain, then ranks samples within each domain by attribution score.
Theoretically, the gap to the optimal subset is linear in the selection size and independent of data pool size.
Empirically, Mixture-then-Select outperforms strong baselines by 20 to 54%, is robust across attribution methods, and works across different models and data pool sizes.
The framework also extends to the data purchasing problem under a budget constraint when sample prices vary.
Our findings suggest that allocating selection across domains offers a promising path toward more efficient and effective data selection for modern LLMs.