Reducing Per-Sample Interference in Stochastic Optimization
Quick summary
arXiv:2607.16261v2 Announce Type: replace-cross Abstract: Modern optimizers combine gradients from the current mini-batch with historical optimization state, such as momentum or adaptive moments. While effective, this standard practice can produce parameter updates that actively increase the loss of individual samples. We term this phenomenon per-sample interference and propose redefining the parameter update as an optimization problem that explicitly minimizes it. Because the exact formulation of the problem is computationally prohibitive, we introduce a highly efficient surrogate. By reducin
Key takeaways
- arXiv:2607.16261v2 Announce Type: replace-cross Abstract: Modern optimizers combine gradients from the current mini-batch with historical optimization state, such as momentum or adaptive moments.
- While effective, this standard practice can produce parameter updates that actively increase the loss of individual samples.
- We term this phenomenon per-sample interference and propose redefining the parameter update as an optimization problem that explicitly minimizes it.
Why it matters
This development shows AI moving deeper into everyday software. Productivity potential should be weighed against price, data permissions, exportability and the preservation of human control.
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