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The paper proposes learning training-example weights with metagradients, then generalizing them through parametric rules that transfer from small models to larger ones. Reward modeling is the test case, and the reported gains come from held-out evaluations and from mixing human and synthetic preference data. Readers should treat these as promising results on one application, not yet a general recipe for curating data at frontier scale.
High-Value Training Data from Noisy, Diverse Sources
arXiv:2610.30421v1 Announce Type: cross Abstract: Training state-of-the-art models increasingly requires learning from large, heterogeneous datasets whose examples vary in quality, domain relevance, and source of supervision. Dataweighting---assigning different weights to training examples---offers a general way to control how such data influences training, but learning effective weights at scale remains challenging. We develop a scalable approach to learning dataweights: given a small, high-quality target dataset, we use metagradients through the training process to efficiently optimize training-example weights directly for performance on the target domain. To scale beyond a fixed dataset and model, we learn parametric weighting rules that generalize to new and streaming examples and transfer from smaller models to substantially larger ones. We study reward modeling as a prototypical application, where preference data may be noisy, adversarial, synthetic, or mismatched with the target domain. Learned dataweights improve held-out performance, automatically adapt reward models to specialized domains, and allow mixtures of human and synthetic supervision to outperform either source alone.
Abstract:Training state-of-the-art models increasingly requires learning from large, heterogeneous datasets whose examples vary in quality, domain relevance, and source of supervision. Dataweighting---assigning different weights to training examples---offers a general way to control how such data influences training, but learning effective weights at scale remains challenging. We develop a scalable approach to learning dataweights: given a small, high-quality target dataset, we use metagradients through the training process to efficiently optimize training-example weights directly for performance on the target domain. To scale beyond a fixed dataset and model, we learn parametric weighting rules that generalize to new and streaming examples and transfer from smaller models to substantially larger ones. We study reward modeling as a prototypical application, where preference data may be noisy, adversarial, synthetic, or mismatched with the target domain. Learned dataweights improve held-out performance, automatically adapt reward models to specialized domains, and allow mixtures of human and synthetic supervision to outperform either source alone.
Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2610.30421 [stat.ML]
(or arXiv:2610.30421v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2610.30421 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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