Every few years, a derivative-free neural network optimization algorithm gets some hype. I'd bet my life savings that none of them ever make an impact.
Derivative-free optimization can be useful for genuinely discontinuous objectives [1], but common neural network objectives are smooth and/or Lipschitz.
The gradient is useful. Instead of trying random directions and hoping that one of them is an improvement, it tells you where to go. The more parameters you have, the more useful it becomes.
A strict complexity gap between gradient-based and derivative-free Lipschitz convex optimization has been suspected for decades and recently proved (using AI, [2]). Neural net optimization is nonconvex, but not radically different.
IMO, a more promising direction is gradient-based optimizers specialized to the neural network structure, like Muon [3].
[1] https://arxiv.org/abs/2202.00817
[2] https://arxiv.org/abs/2607.13335
[3] https://jeremybernste.in/writing/deriving-muon
Derivative-free optimization can be useful for genuinely discontinuous objectives [1], but common neural network objectives are smooth and/or Lipschitz.
The gradient is useful. Instead of trying random directions and hoping that one of them is an improvement, it tells you where to go. The more parameters you have, the more useful it becomes.
A strict complexity gap between gradient-based and derivative-free Lipschitz convex optimization has been suspected for decades and recently proved (using AI, [2]). Neural net optimization is nonconvex, but not radically different.
IMO, a more promising direction is gradient-based optimizers specialized to the neural network structure, like Muon [3].
[1] https://arxiv.org/abs/2202.00817
[2] https://arxiv.org/abs/2607.13335
[3] https://jeremybernste.in/writing/deriving-muon