Optimizers for Diffusion on Dynamical Systems

In this AI Research Roundup episode, Alex discusses the paper:
'Optimization Benchmark for Diffusion Models on Dynamical Systems(2510.19376v1)'
This work benchmarks modern optimization algorithms for training diffusion models to denoise flow trajectories from 2D Navier–Stokes simulations. The authors find Muon and SOAP outperform AdamW with about 18% lower final loss. They evaluate a U-Net score model trained with the DDPM objective and revisit learning-rate schedule effects and the Adam vs. SGD performance gap. Results suggest optimizers validated on LLMs can transfer effectively to diffusion-model training on dynamical-system data.
Paper URL: https://arxiv.org/pdf/2510.19376

#AI #MachineLearning #DeepLearning #DiffusionModels #Optimizers #DynamicalSystems #NavierStokes #DDPM

Видео Optimizers for Diffusion on Dynamical Systems канала AI Research Roundup