# LEV

Decision models that answer fixed-option questions usually score every option in one forward pass, with a depth fixed at training time, so easy and hard questions cost the same. LEV swaps in a language-model backbone pretrained as a loop, reads the options after every step and trains all steps jointly, so one checkpoint answers at any depth; on JevBench the 2.6B model rises from 63.2% at depth one to 76.2% at depth four, mostly on hard questions. A small stopper sets depth per question with a threshold fitted on unlabelled requests, saving 15% to 24% of the computation at under one accuracy point.

Decision models that answer fixed-option questions usually score every option in one forward pass, with a depth fixed at training time, so easy and hard questions cost the same. LEV swaps in a language-model backbone pretrained as a loop, reads the options after every step and trains all steps jointly, so one checkpoint answers at any depth; on JevBench the 2.6B model rises from 63.2% at depth one to 76.2% at depth four, mostly on hard questions. A small stopper sets depth per question with a threshold fitted on unlabelled requests, saving 15% to 24% of the computation at under one accuracy point.