FFN Activation Functions › Which Activation Should You Use?

The choice of activation function depends on your model architecture and use case. Here's a practical guide:

Use ReLU when:

• You need maximum computational efficiency and simplicity
• You're building a custom architecture and want a well-understood baseline
• Your model is small enough that the dead neuron problem is manageable
• You're working with older codebases or frameworks that expect ReLU

Use GELU when:

• You're building an encoder model (BERT, RoBERTa, ELECTRA style)
• You want compatibility with pretrained encoder models
• You prioritize smooth gradients over computational efficiency
• You're fine-tuning an existing GELU-based model

Use SiLU when:

• You're building a decoder model (GPT, LLaMA, Mistral style)
• You're using gated linear units (SwiGLU, GeGLU) in your FFN
• You want the slight performance edge that modern LLMs have demonstrated
• You need a smooth activation but prefer simpler computation than GELU

The empirical differences between GELU and SiLU are often small. Unless you're training at massive scale where every fraction of a percent matters, either smooth activation will likely work well. The more important choice is between ReLU (with its dead neuron risk) and the smooth alternatives.

When fine-tuning a pretrained model, you should essentially always match the activation used during pretraining. Changing from GELU to SiLU in the middle of a model's lifecycle would invalidate the learned weight distributions, since those weights were optimized under the original activation's gradient behavior. The weights are not activation-function agnostic: they encode information in a way that is calibrated to how the original activation transforms inputs. Switching activations after pretraining would require substantial additional training to re-adapt all the weights.