When Does Generating More Help? Disentangling Fixed-Source Synthesis from Source Expansion in Synthetic Data Scaling
arXiv:2607.01727v1 Announce Type: new Abstract: Synthetic data can be scaled along two routes: Source Expansion (SE), which enlarges the source by adding seed materials or generators, and Fixed-Source Synthesis (FSS), which holds the source fixed and scales the generation budget. Existing scaling studies typically expand the source as the data grows, conflating SE with FSS and leaving FSS underexplored. We isolate FSS by holding the seed-question pool and teacher model fixed, varying only the per-question response budget under Rejection Sampling (RS). We adapt the rectified scaling law to FSS,
The increasing demand for synthetic data in AI development necessitates a deeper understanding of efficient scaling methods, moving beyond current conflated approaches.
Optimizing synthetic data generation through Fixed-Source Synthesis could significantly reduce computational costs and improve data efficacy, accelerating AI model development.
By isolating Fixed-Source Synthesis, researchers can now more precisely evaluate the efficiency of generating more data from a constrained source, rather than just expanding the source itself.
- · AI model developers
- · Cloud computing providers
- · Data generation tool vendors
- · Companies reliant on expensive data acquisition
More efficient and cost-effective synthetic data generation processes will emerge.
Faster iteration and deployment of AI models due to readily available and optimized synthetic datasets.
A potential shift in AI funding towards compute optimization rather than raw data acquisition, impacting data marketplace models.
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Read at arXiv cs.CL