Published in 2025, this paper "splits" the problem of in LLM embeddings.
It shows that Adam minimizes a specific form of sharpness —specifically the trace of the square root of the Hessian—which is fundamentally different from how SGD behaves. 4. Better Embeddings with Coupled Adam Splitting Adam
It argues that Adam's second moment actually causes word representations to become narrow and directional (anisotropic). Published in 2025, this paper "splits" the problem
This version of ADAM is used for "splitting" an elite population of particles to better sample rare events or solve multi-objective optimization problems. Better Embeddings with Coupled Adam It argues that
A more recent and highly regarded paper (2025) investigates what happens when Adam "wanders" around the manifold of minimizers.
Based on your interest in "Splitting Adam," you are likely referring to research surrounding the widely used in machine learning. There isn't one single paper with that exact title, but several "interesting" papers analyze splitting the algorithm's components or its behavior in complex ways: 1. The Sign, Magnitude and Variance of Stochastic Gradients
If you are coming from a statistics or rare-event simulation background, "ADAM" refers to .