Two-Time-Scale Learning Dynamics: A Population View of Neural Network Training
Population-based learning paradigms, including evolutionary strategies, Population-Based Training (PBT), and recent model-merging methods, combine fast within-model optimisation with slower population-level adaptation. Despite their empirical success, a general mathematical description of the resulting collective training dynamics remains incomplete. We introduce a theoretical framework for neural network training based on two-time-scale population dynamics. We model a population of neural networks as an interacting agent system in which network parameters evolve through fast noisy gradient updates of SGD/Langevin type, while hyperparameters evolve through slower selection--mutation dynam...
| Authors | Giacomo Borghi, Hyesung Im, Lorenzo Pareschi |
|---|---|
| Primary category | cs.LG |
| Published | 20 March 2026 |
| Last updated at source | 25 March 2026 |
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