Beyond Backpropagation: Monte Carlo Method Can Train Deep Neural Networks
Backpropagation (BP) dominates deep learning training, but its reliance on gradients brings inherent troubles -- vanishing and exploding gradients. The pursuit of gradient-free methods has long been a goal in the field of artificial intelligence. This paper shows that indeed the simplest Monte Carlo algorithm implemented on a single GPU -- randomly mutate a parameter, keep it if the loss decreases, otherwise retry -- can practically train deep networks. This gradient-free method does not even need common techniques such as batch normalization or residual connections to directly train sufficiently deep networks. More remarkably, its flexibility extends to several nontrivial scenarios: it e...
| Authors | Hong Zhao |
|---|---|
| Primary category | cs.LG |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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