One '+x' That Made 100-Layer Networks Trainable: ResNet Skip Connections

Residual blocks can improve deep networks by reducing the need for intermediate layers and improving the ability to learn the correct correction for input values.
The +1 in the residual block formula keeps the gradient intact, allowing the block to learn the correct correction without vanishing.
Residual blocks are particularly effective in deep networks, where the 2015 ResNet paper trained 152-layer networks, which were previously deeper than what worked before.
The +1 keeps gradients healthy in modern LLMs, where skip connections are now everywhere and the 2015 ResNet paper trained 100-layer networks.

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