Backpropagation-Free Trunk Training via the Split Forward Gradients
Backpropagation makes training deep networks memory intensive because it must store intermediate activations. Forward-mode methods avoid this cost, but their gradient estimates become increasingly noisy as the number of trained parameters grows. We introduce Split Forward Gradient (Split-FG), which splits a network at an intermediate representation: it computes the output head gradient exactly and estimates only the trunk gradient with a Jacobian--vector product. This reduces estimator variance and requires no backward pass through the trunk, while retaining an Adam-style convergence guarantee. Our experiments reveal an important practical failure mode. On WikiText-103, naive forward-grad...
| Authors | Tian Qin, Wei-Min Huang |
|---|---|
| Primary category | stat.ML |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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