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COREM: Cosine-Relation Momentum Reshaping with Stateful Writeback

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Computer Science > Machine Learning

arXiv:2609.22487 (cs)
[Submitted on 18 Sep 2026]

Title:COREM: Cosine-Relation Momentum Reshaping with Stateful Writeback

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Abstract:Matrix-valued optimizer states may contain relational structure that is not captured by treating their entries independently. We study whether relations within matrix-valued optimizer states can be exploited to improve optimization. To this end, we introduce a unit-relation-transform abstraction and instantiate it as COREM, a Cosine-Relation Momentum Reshaping method with stateful writeback. COREM partitions the momentum state into update units, computes cosine relations among them, and uses these relations to reshape the momentum before writing the transformed state back to the optimizer. This stateful mechanism allows the reshaped momentum to affect not only the current update but also future optimization dynamics. We evaluate COREM on CIFAR-10 with an MLP and on enwik8 with a Transformer. Compared with Muon, COREM shows lower early-stage step efficiency but stronger improvement in the mid-to-late stages of training, achieving better final validation performance on CIFAR-10 and comparable final performance on enwik8. Spectral diagnostics on enwik8 show that COREM consistently increases entropy effective rank and reduces the concentration of singular energy in dominant modes, while preserving an anisotropic spectrum. For square matrix updates, COREM requires approximately 13.3% of the transformation FLOPs of Muon with five Newton-Schulz iterations.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2609.22487 [cs.LG]
  (or arXiv:2609.22487v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2609.22487
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yan Wang [view email]
[v1] Fri, 18 Sep 2026 18:51:54 UTC (281 KB)
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