arXiv:2605.23061v1 Announce Type: new 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 explicit schedules, yet SF-AdamW, the current state-of-the-art anytime optimizer, consistently underperforms well-tuned AdamW baselines. We propose SF-NorMuon, a schedule-free spectral optimizer that closes this gap: with a single hyperparameter configuration, SF-NorMuon matches or exceeds tuned AdamW on 125M and 772M par
The continuous drive for more efficient and robust AI training methods, especially as model sizes grow, necessitates advancements in optimization algorithms.
Improved anytime optimizers reduce the computational cost and complexity of training large neural networks, making advanced AI more accessible and flexible.
The reliance on fixed learning-rate schedules, and the accompanying re-tuning costs, could diminish, leading to faster iteration and deployment of AI models.
- · AI researchers
- · Cloud providers
- · AI development platforms
- · Large language model developers
- · Organizations with limited compute budgets using inefficient training methods
Neural network training becomes more efficient and less dependent on hyperparameter tuning.
This could accelerate the development and deployment of larger and more complex AI models across various applications.
Reduced compute costs for model training might lower barriers to entry for AI innovation, potentially fostering a more diverse AI ecosystem.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG