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Synthetic data's impact on SGD generalization studied in new arXiv paper

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv explores the impact of synthetic data on the generalization capabilities of Stochastic Gradient Descent (SGD) in high-dimensional linear regression. The research identifies that while mixed training protocols can lead to significant model collapse, a two-stage training approach, where synthetic data is used only in the initial phase, can avoid this performance floor. The study also derives scaling laws for both protocols, suggesting that larger models might exacerbate synthetic data degradation in mixed training, but high-quality pretraining with synthetic data can improve bias in two-stage methods. AI

IMPACT Highlights how training protocols critically influence the effectiveness of synthetic data, impacting model performance and scaling.

RANK_REASON Academic paper on synthetic data and SGD generalization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Synthetic data's impact on SGD generalization studied in new arXiv paper

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Academic paper on synthetic data and SGD generalization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jichu li, Difan Zou ·

    Learning with Synthetic Data via SGD in High-Dimensional Linear Regression

    arXiv:2609.09572v1 Announce Type: new Abstract: Synthetic data has become a promising way to scale model training beyond limited human-generated data but it may also induce strong model collapse (Dohmatob et al., 2024), where any fixed fraction of synthetic data prevents model pe…

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