Autoencoder training issues often stem from improper input scaling, overfitting, latent space size, loss function choice, optimizer settings, and model capacity. Key techniques include input normalization, dropout regularization, careful selection of latent dimensions, appropriate loss functions like binary crossentropy, and tuning learning rates.