Double preconditioning (DoPr) improves downstream task performance in test-time feedback settings without consistent gains in validation loss.
arXiv preprint arXiv:2312.05705 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Gradient Smoothing applies depth-wise smoothing to optimizer updates from base methods like Adam, yielding consistent gains in optimization and generalization on language, RL, diffusion, and vision tasks.
citing papers explorer
-
Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss
Double preconditioning (DoPr) improves downstream task performance in test-time feedback settings without consistent gains in validation loss.
-
Gradient Smoothing: Coupling Layer-wise Updates for Improved Optimization
Gradient Smoothing applies depth-wise smoothing to optimizer updates from base methods like Adam, yielding consistent gains in optimization and generalization on language, RL, diffusion, and vision tasks.