SpecMRL beats full-dimensional MRL with half the embedding dimensions
Zhongxin Chen · hf · 2026-10-20
A paper from Renmin University's Gaoling School, Wuhan University and Lenovo, SpecMRL notes that Matryoshka Representation Learning (MRL) supports flexible representation sizes but lacks an explicit constraint ensuring the leading prefix is a good compact encoding.
- Extends MRL with a parameter-derived spectral basis organized by feature importance, with prefix readout performed in the learned spectral space
- On MTEB, SpecMRL outperforms standard MRL at all evaluated reduced dimensions, with larger gains under more aggressive compression
- Surpasses full-dimensional MRL using only half the embedding dimensions, extending MRL's performance frontier
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