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Training with ML-labeled Data via Stochastic First- and Zeroth-Order Optimization

Maria-Eleni Sfyraki, Jun-Kun Wang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26790 · cited by 0 works (OpenAlex) · open-access PDF

Indonesian

Latihan model pembelajaran mesin (ML) adalah proses optimalisasi yang biasanya membutuhkan serangkaian contoh dengan label dasar-sebenar sebelumnya. Mendapatkan kebenaran dasar ini biasanya membutuhkan usaha manusia yang melelahkan dan bisa mahal. Skalabilitas label manusia menjadi masalah ketika ada sejumlah besar titik data yang perlu dilabel. Untuk mengatasi masalah praktis ini, kami mempertimbangkan pengaturan optimasian di mana subset sampel memiliki label dasar-sebenar, sementara titik data yang tersisa dilabelkan oleh prediktor ML, yang dapat berpotensi mengalokasikan label yang salah ke titik-titik tersebut. Kami mengusulkan metode optimasi stochastic dari urutan pertama dan urutan nol untuk pelatihan pada dataset yang terdiri dari campuran sampel yang dilabelkan benar dan ML. Kami juga melakukan studi empiris yang komprehensif dari melatih model regresi linear hingga menyesuaikan model bahasa besar

Machine translation (Meta M2M100); the English original follows.

Abstract

Training a machine learning (ML) model is an optimization process that typically requires a set of examples with ground-truth labels beforehand. Obtaining these ground truths usually requires laborious human effort and can be costly. The scalability of human labeling becomes a concern when there are a large number of data points that need to be labeled. To address this practical issue, we consider an optimization setting where a subset of samples has ground-truth labels, while the remaining data points were labeled by an ML predictor, which could potentially assign incorrect labels to those points. We propose both first-order and zeroth-order stochastic optimization methods for training on datasets consisting of a mix of true-labeled and ML-labeled samples. We further conduct comprehensive empirical studies from training a linear regression model to fine-tuning a large language model (LLM) to showcase the effectiveness of the proposed algorithms for training with ML-labeled data.

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Topics: Computer science, Machine learning, Training set, Training (meteorology), Process (computing), Set (abstract data type)

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