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New optimizer SF-NorMuon matches AdamW performance without schedules

By PulseAugur Editorial · [2 sources] ·

Researchers have developed SF-NorMuon, a new schedule-free spectral optimizer that matches or surpasses the performance of traditional AdamW optimizers. This advancement addresses a key limitation in current anytime training methods, where schedule-free approaches often underperform. SF-NorMuon's ability to achieve high-quality training checkpoints at any point without pre-defined horizons makes it a more practical tool for open-ended continual learning. AI

IMPACT Enables more flexible and efficient neural network training by allowing high-quality checkpoints at any stage without fixed schedules.

RANK_REASON The cluster contains an academic paper detailing a new optimization method for neural network training.

Read on arXiv stat.ML →

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

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Anuj Apte, Pranav Deshpande, Niraj Kumar, Shouvanik Chakrabarti, Junhyung Lyle Kim ·

    Anytime Training with Schedule-Free Spectral Optimization

    arXiv:2605.23061v1 Announce Type: cross Abstract: Standard neural network training relies on learning-rate schedules tied to a fixed horizon, leading to strong path dependence and costly re-tuning as data availability changes. Schedule-Free (SF) methods address this by removing e…

  2. arXiv stat.ML TIER_1 English(EN) · Junhyung Lyle Kim ·

    Anytime Training with Schedule-Free Spectral Optimization

    Standard neural network training relies on learning-rate schedules tied to a fixed horizon, leading to strong path dependence and costly re-tuning as data availability changes. Schedule-Free (SF) methods address this by removing explicit schedules, yet SF-AdamW, the current state…

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