Fixed-point convergence provides a natural halting mechanism for iterative reasoning models, letting them use fewer steps on easy problems and more on hard ones—without explicit stopping signals.
This paper introduces FPRM, a looped Transformer that solves reasoning tasks by repeatedly applying the same layer until reaching a fixed point, automatically stopping when the model's internal state stabilizes. The approach addresses signal propagation problems in deep networks and adapts computation based on task difficulty.