Definition
A normalization trick that lets a recurrent language model stay stable forward while still training cleanly backward.
A normalization scheme in HRM-Text placing stabilizing norms at the exit of every recurrent step; combined with truncated backpropagation, gives PostNorm-style activation bounding on the forward pass and PreNorm-style gradient flow on the backward pass.