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Study: Adaptive RK optimizers offer limited gains over Adam in neural network training

By PulseAugur Editorial · [2 sources] ·

A new study investigates the effectiveness of adaptive Runge-Kutta (RK) optimizers for neural network training, comparing them against standard Adam. The research found that under a strict compute-matched protocol, RK-Adam variants did not outperform plain Adam in reducing training loss and sometimes even performed worse. The study suggests that the adaptivity in these RK methods is often illusory, with step sizes frequently maxing out and producing nearly identical results across a wide range of tolerance settings. While a repaired version showed promise in reducing training loss, this gain was fragile and did not translate to improved test accuracy, indicating that cheaper first-order methods may offer comparable or superior results. AI

IMPACT Suggests that advanced optimization techniques may not offer significant benefits over established methods like Adam for neural network training.

RANK_REASON Academic paper detailing a study on optimization methods for neural networks.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Study: Adaptive RK optimizers offer limited gains over Adam in neural network training

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Akhilesh Gogikar ·

    Adaptive Runge-Kutta Step Control Buys Training Loss, Not Generalization: An Honest Compute-Matched Study of RK-Adam Optimizers

    Interpreting optimizers as gradient-flow discretizations has motivated applying higher-order Runge-Kutta (RK) integrators to neural networks. We build a representative Adam variant (Bogacki-Shampine 3(2) RK pair, FSAL reuse, local-error step control) and evaluate it under a stric…

  2. arXiv stat.ML TIER_1 English(EN) · Akhilesh Gogikar ·

    Adaptive Runge-Kutta Step Control Buys Training Loss, Not Generalization: An Honest Compute-Matched Study of RK-Adam Optimizers

    arXiv:2607.14516v1 Announce Type: cross Abstract: Interpreting optimizers as gradient-flow discretizations has motivated applying higher-order Runge-Kutta (RK) integrators to neural networks. We build a representative Adam variant (Bogacki-Shampine 3(2) RK pair, FSAL reuse, local…

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