DART-ES, a new Evolution Strategies fine-tuning method from researchers publishing on arXiv as 2610.06993v1, improves average accuracy from 72.07% to 73.53% over standard ES and exceeds GRPO's 73.26% …
A developer released a self-contained C++ testbed on GitHub that compresses RGB textures as a shared low-resolution latent texture plus a tiny MLP decoder, trained entirely with Evolution Strategies r…
A developer prototyped a toy neural texture compression system trained entirely with Evolution Strategies, avoiding backpropagation and derivatives. The setup pairs a 128×128×8 latent texture with a 1…
Researchers at ICML 2026 presented a unified framework showing that parametric and nonparametric black-box optimizers are variations of a single update equation. By bridging this theoretical gap, they…