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Anon optimizer offers tunable adaptivity, outperforming Adam and SGD on key tasks

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have introduced Anon, a novel optimizer designed to bridge the performance gap between adaptive methods like Adam and non-adaptive methods like SGD. Anon features continuously tunable adaptivity, allowing it to interpolate and even extrapolate beyond existing optimizer behaviors. The optimizer incorporates an incremental delay update mechanism to ensure convergence across its adaptivity spectrum and has demonstrated superior performance on image classification, diffusion, and language modeling tasks. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a new optimizer that could improve training efficiency and performance for large-scale models in image, diffusion, and language tasks.

RANK_REASON Academic paper introducing a novel optimizer with theoretical guarantees and empirical results.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Yiheng Zhang, Kaiyan Zhao, Shaowu Wu, Yiming Wang, Jiajun Wu, Leong Hou U, Steve Drew, Xiaoguang Niu ·

    Anon: Extrapolating Optimizer Adaptivity Across the Real Spectrum

    arXiv:2605.02317v1 Announce Type: cross Abstract: Adaptive optimizers such as Adam have achieved great success in training large-scale models like large language models and diffusion models. However, they often generalize worse than non-adaptive methods, such as SGD on classical …

  2. arXiv cs.LG TIER_1 · Xiaoguang Niu ·

    Anon: Extrapolating Optimizer Adaptivity Across the Real Spectrum

    Adaptive optimizers such as Adam have achieved great success in training large-scale models like large language models and diffusion models. However, they often generalize worse than non-adaptive methods, such as SGD on classical architectures like CNNs. We identify a key cause o…

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