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Explicitly modeling cross-layer information flow as a memory retrieval problem unlocks a linear-time attention alternative that outperforms existing methods.
Spectral inheritance allows for a dramatic reduction in training steps while enhancing model accuracy, challenging traditional optimization methods in RLVR.
SLORR achieves substantial model compressibility with under 1% training overhead, outperforming traditional regularization methods in preserving performance.