AdamX: Cosine similarity meets gradient descent

Abstract:We introduce AdamX, a first-order optimizer that incorporates cosine similarity as an adaptive mechanism for controlling update magnitudes. The proposed method is scalable, model-agnostic, and straightforward to integrate into existing training pipelines. We further introduce a variance rectification scheme that promotes smoother optimization during the early stages of training. Overall, we provide empirical evidence that AdamX achieves competitive convergence rates across a range of benchmark datasets and architectures. Performance is evaluated in terms of the number of epochs required to reach predefined performance thresholds under a fixed hyperparameter budget. Code and Experiments available at: this https URL.

Machine Learning (cs.LG); Optimization and Control (math.OC)

arXiv:2609.11867 [cs.LG]

(or arXiv:2609.11867v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.11867 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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