PyTorch Conference Europe 2026

Backpropagation is not the only mechanism for training deep networks. This talk presents a compact, implementation-driven map of backpropagation-free training methods, organized around representative algorithms that expose key design trade-offs.

We focus on four families: Difference Target Propagation (target-based credit assignment), Direct Feedback Alignment (random feedback without weight transport), local loss / greedy layerwise training (strictly local objectives), and Forward-Forward learning as a forward-only alternative. Each is treated as a minimal working pattern rather than a full system.

For each representative, we answer the same practical questions: what learning signal is propagated, what intermediate state must be stored, how parameters are updated, and what limits scalability on modern accelerators. The emphasis is on PyTorch-level mechanics—explicit update loops, local objectives, and training without autograd—rather than derivations.

The goal is to give practitioners a clear mental model of the backprop-free design space and concrete patterns for experimenting with these methods in real PyTorch training pipelines.