SAE Feature Direction Loss
The SAE Feature Absorption and co-occurrence problems cause the model to learn "broken latents". While tied SAEs have cleaner representations due to identical encoder and decoder weights, issues still arise when there are insufficient latents for concepts like parent-child relationships.
To mitigate this mixing phenomenon, an auxiliary loss function (squared cosine similarity between inputs and feature directions at low activation states) is introduced to encourage single peaks in activation strength.
Tanh loss
Achieves Pareto-optimality by "minimizing feature activations while maintaining low output error"
