Why are residual networks easier to train?

Residual networks (ResNets) are easier to train because their shortcut connections let each block learn a correction to its input instead of learning an entirely new mapping. This makes optimization more reliable as networks become deeper.

Why does this matter?

In a very deep *plain* network, adding more layers can unexpectedly increase training error. This is called the degradation problem—it is not simply overfitting.

Residual blocks address it in two important ways:

• Learn changes, not replacements: A block computes y = x + F(x). If no additional transformation is useful, it can learn F(x) ≈ 0 and largely preserve the input. Extra layers therefore have an easier path to behaving like identity mappings.
• Improve gradient propagation: The shortcut provides a direct path back to earlier layers during backpropagation, reducing reliance on gradients passing through every intermediate transformation.

Simple intuition

Imagine adding ten more layers to an already effective network. In a plain network, those layers must collectively learn not to damage useful features. In a ResNet, they can instead learn small improvements while retaining a shortcut for the existing information.

Residual connections do not guarantee better accuracy or eliminate all optimization problems, but they make very deep networks substantially more practical.

For the underlying mechanism, see “How does a residual block work?” and “How do skip connections help train deep neural networks?”.

In short: ResNets are easier to optimize because shortcut paths preserve information, support gradient flow, and make identity-like behavior easier to learn.