TANDEM: Bi-Level Data Mixture Optimization with Twin Networks
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Computer Science > Machine Learning
Title:TANDEM: Bi-Level Data Mixture Optimization with Twin Networks
Abstract:The capabilities of large language models (LLMs) significantly depend on training data drawn from various domains. Optimizing domain-specific mixture ratios can be modeled as a bi-level optimization problem, which we simplify into a single-level penalized form and solve with twin networks: a proxy model trained on primary data and a dynamically updated reference model trained with additional data. Our proposed method, Twin Networks for bi-level DatA mixturE optiMization (TANDEM), measures the data efficacy through the difference between the twin models and up-weights domains that benefit more from the additional data. TANDEM provides theoretical guarantees and wider applicability, compared to prior approaches. Furthermore, our bi-level perspective suggests new settings to study domain reweighting such as data-restricted scenarios and supervised fine-tuning, where optimized mixture ratios significantly improve the performance. Extensive experiments validate TANDEM's effectiveness in all scenarios.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.04401 [cs.LG] |
| (or arXiv:2606.04401v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.04401
arXiv-issued DOI via DataCite (pending registration)
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