Definition
When training an iterative refinement model teaches the underlying network to produce the final answer in one pass, making the refinement unnecessary at inference.
An emergent phenomenon in attractor-model training where the high-capacity backbone learns to initialize the residual stream at the fixed point itself, so the equilibrium solver requires few or zero iterations at test time.