A very important milestone for me in the AI field.

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Three days ago, I officially released my first AI research paper: STAM (Stable Training with Adaptive Momentum).

STAM introduces a new optimizer for deep learning and focuses on:

• improving training stability,
• reducing resource consumption during training,
• and addressing several limitations found in optimizers like Adam, AdamW, and Muon.

The paper explains what makes STAM different, the problems it aims to solve, and includes comparisons with existing optimizers and training results.

The research paper is currently available on SSRN, and it has reached a ranking of around 646K so far.

What matters most to me is not numbers, but having AI engineers, researchers, and specialists read the paper and share honest technical feedback and criticism.

I consider STAM one of the biggest projects I’ve ever worked on, and I plan to continue improving and developing it further. I would genuinely appreciate hearing opinions from researchers and experienced people in the AI community about the paper, the optimizer design, and the reported results compared to other optimizers.

Research paper:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6699059

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