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GONO optimizer adapts Adam's momentum using directional consistency for better convergence

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced the GONO framework, an optimization signal designed to improve deep learning training by addressing the decoupling of directional alignment and loss convergence. Unlike existing optimizers that primarily rely on magnitude, GONO adapts momentum based on the temporal consistency of gradient directions. This approach aims to better distinguish between plateaus and genuine convergence, potentially leading to more efficient training. AI

IMPACT Introduces a novel optimization signal that could enhance training efficiency for deep learning models.

RANK_REASON The cluster contains an arXiv preprint detailing a new optimization framework for deep learning.

Read on arXiv cs.LG →

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

GONO optimizer adapts Adam's momentum using directional consistency for better convergence

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The cluster contains an arXiv preprint detailing a new optimization framework for deep learning.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Victor Daniel Gera ·

    Directional Consistency as a Complementary Optimization Signal: The GONO Framework

    arXiv:2605.06575v1 Announce Type: new Abstract: We identify and formalize an underexplored phenomenon in deep learning optimization: directional alignment and loss convergence can be decoupled. An optimizer can exhibit near-perfect directional consistency (cc_t -> 1, measured via…

  2. arXiv cs.AI TIER_1 English(EN) · Victor Daniel Gera ·

    Directional Consistency as a Complementary Optimization Signal: The GONO Framework

    We identify and formalize an underexplored phenomenon in deep learning optimization: directional alignment and loss convergence can be decoupled. An optimizer can exhibit near-perfect directional consistency (cc_t -> 1, measured via consecutive gradient cosine similarity) while t…

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