Most state-of-the-art language models undergo chat-tuning, however its effect is still poorly understood.The recently introduced crosscoder – a variant of sparse autoencoders – provides a tool to understand how chat-tuning changes language models by systematically comparing the pre-trained and chat-tuned models' latent spaces.By analyzing the crosscoder training loss, we identify two theoretical challenges, termed Complete Shrinkage and Latent Decoupling, that could lead to misclassification of latents as chat-tuning-specific. We propose Latent Scaling, a technique to detect and filter these issues and empirically observe that it identifies highly interpretable latents appearing unique to the chat model's behavior.