2/18/2026 | USA | technology | ✓ Verified - arxiv.org

On Surprising Effectiveness of Masking Updates in Adaptive Optimizers

#adaptive optimizer #RMSProp #parameter update masking #large language model #curvature regularization #state‑of‑the‑art #loss landscape smoothing #machine learning research

📌 Key Takeaways

The training of large language models (LLMs) relies heavily on dense adaptive optimizers.
A new approach shows that randomly masking parameter updates can be highly effective.
A masked variant of RMSProp outperforms state‑of‑the‑art optimizers.
The random masking introduces curvature‑dependent geometric regularization that smooths the optimization landscape.

📖 Full Retelling

Researchers in the machine learning community have shown that randomly masking parameter updates in adaptive optimizers can significantly improve the training of large language models. The study, published a few weeks ago on arXiv, demonstrates that a masked variant of RMSProp consistently outperforms recent state‑of‑the‑art optimizers by inducing a curvature‑dependent geometric regularization that smooths the loss landscape. This finding challenges the prevailing assumption that dense adaptive optimizers with sophisticated preconditioners are essential for large‑scale model training.

🏷️ Themes

Machine learning optimization, Large language model training, Adaptive optimizers, Regularization techniques, Curvature-aware algorithms

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Deep Analysis

Why It Matters

The study shows that simple random masking of parameter updates can outperform complex adaptive optimizers, challenging the prevailing reliance on dense preconditioners. This finding could simplify training pipelines and reduce computational overhead for large language models.

Context & Background

Adaptive optimizers like Adam and RMSProp dominate LLM training
Preconditioners add significant computational cost
Random masking introduces curvature-dependent regularization

What Happens Next

Researchers may explore masking strategies as a lightweight alternative to sophisticated optimizers. Future work could investigate theoretical foundations and practical implementations across different model architectures.

Frequently Asked Questions

What is random masking in this context?

It refers to randomly zeroing out a subset of parameter updates during training, reducing the number of updates applied at each step.

Does masking reduce training speed?

Because fewer updates are computed, it can lower memory usage and computation, potentially speeding up training while maintaining or improving performance.

Is this approach applicable to all models?

Initial experiments focus on large language models, but the concept may generalize to other deep learning tasks, pending further validation.

Original Source

              arXiv:2602.15322v1 Announce Type: cross 
Abstract: Training large language models (LLMs) relies almost exclusively on dense adaptive optimizers with increasingly sophisticated preconditioners. We challenge this by showing that randomly masking parameter updates can be highly effective, with a masked variant of RMSProp consistently outperforming recent state-of-the-art optimizers. Our analysis reveals that the random masking induces a curvature-dependent geometric regularization that smooths the 
            

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Source

arxiv.org