Quasi-Newton methods scaled to deep learning without line searches
SoftServe addresses a fundamental bottleneck in deep learning optimization by adapting quasi-Newton methods, traditionally confined to convex problems, for non-convex neural network training at scale. The approach derives positive-definite curvature estimates without requiring line searches or manual corrections, using coupled Newton-Schulz iterations to handle massive parameter counts. This matters because second-order methods have remained impractical for large models despite superior convergence properties. Diagonal and Kronecker-factored variants suggest a path toward competitive second-order training without prohibitive computational overhead, potentially reshaping how practitioners balance convergence speed against per-step cost.
Modelwire context
ExplainerSoftServe's key novelty is avoiding the two traditional blockers of quasi-Newton methods at scale: it derives positive-definite curvature estimates without line searches or manual corrections, and it handles massive parameter counts through factored variants. The paper doesn't claim to match first-order speed per step, only to offer better convergence per unit of wall-clock time.
This sits alongside recent work on training efficiency but targets a different lever than prior coverage. The Probe-Space Preconditioning paper from late September also tackled memory overhead in large-model training by reallocating compute, but through zero-order methods. SoftServe takes the opposite direction: it assumes gradient access and invests in curvature information to reduce iteration count. Together, these represent a bifurcation in how researchers are attacking the training bottleneck. The Distance-KV and EAServe papers addressed inference efficiency, not training. SoftServe is closer in spirit to the ZFO work from today, which also decouples direction from step selection, though ZFO remains first-order while SoftServe commits to second-order curvature.
If practitioners report that diagonal or Kronecker-factored SoftServe variants achieve convergence in fewer total iterations than Adam on models larger than 7B parameters within the next six months, the method has crossed from theoretical promise to practical adoption. If adoption remains confined to smaller models or research settings, the per-step overhead likely still outweighs the iteration savings in production.
Coverage we drew on
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MentionsSoftServe · Berglund et al. · Newton-Schulz iteration
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