Tag
This paper investigates how sign-branched repetition penalties cause structured-output corruption and gauge dependence across different models and inference frameworks, providing measurements and comparisons with alternative repetition controls.
Liquid AI introduces Antidoom, a method using Final Token Preference Optimization to reduce repetitive doom loops in small reasoning models during inference, cutting loop rates from 10.2% to 1.4% while improving evaluation scores.