arXiv: cs.AI preprintsInternational6 November 2026
Extending Reasoning in Typed Decision Models via Looping
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arXiv:2611.07736v1 Announce Type: cross Abstract: Typed decision models choose among a fixed set of answer options in a single forward pass, so unlike generative language models they cannot spend more test-time compute on hard inputs by generating more tokens. Looping a block of layers offers this compute in latent space without adding parameters, but prior work found no gain from loops retrofitted into conventionally pretrained models. We show that such loops help when trained together with the decision model. Our method, RELOAD (retrofitted loops for adaptive decisions), fine-tunes a pretrai
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