Objective-Specific Privileged Bases via Full-Prefix Matryoshka Learning

arXiv:2605.09160v2 Announce Type: replace Abstract: Learned representations are often invariant to rotational transformations, leaving individual dimensions non-identifiable and interchangeable. We study how Matryoshka Representation Learning (MRL) induces a task-aligned privileged basis distinct from variance-based or regularizer-induced orderings. In the linear setting, we prove that full-prefix MRL recovers the ordered principal directions, and can be computed efficiently using shared statistics. Empirically, we demonstrate that MRL yields consistent per-dimension structure aligned with tas

Source: arXiv cs.LG — read the full report at the original publisher.

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