AWhat changed
Used warm-started subspace iteration to build low-rank gradient approximations compatible with all-reduce, combining them with residual feedback and momentum for practical distributed training.
Scientific scope
Used warm-started subspace iteration to build low-rank gradient approximations compatible with all-reduce, combining them with residual feedback and momentum for practical distributed training. Inherits power iteration, low-rank compression and error feedback. One iteration is not guaranteed to equal the best SVD approximation for every gradient; warm-start quality and rank matter. Orthogonalization/compression add cost; vectors may remain uncompressed. Source-specific wall-clock gains are not universal network/hardware speedups or lossless gradients.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits power iteration, low-rank compression and error feedback. One iteration is not guaranteed to equal the best SVD approximation for every gradient; warm-start quality and rank matter. Orthogonalization/compression add cost; vectors may remain uncompressed. Source-specific wall-clock gains are not universal network/hardware speedups or lossless gradients.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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