Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers
Looped architectures provide an inductive bias toward learning step-by-step procedures for tasks that require compositional reasoning. The number of effective layers reached by looping determines the quality of the solution these models find. Like deep architectures, looped architectures are prone to a signal propagation problem induced by depth as the halting decision is postponed. In this paper, we address this signal propagation issue using pre-norm layers and residual scaling. Building on these architectural modifications, we propose FPRM, a Transformer-based Fixed-Point Reasoning Model that uses fixed-point convergence as an end-to-end halting mechanism in a looped architecture. We s...
| Authors | Sajad Movahedi, Vera Milovanović, Shlomo Libo Feigin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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