Looped Transformers naturally produce weak-to-strong prediction pairs across recurrent passes; contrasting them during decoding improves quality and enables halving compute with no training needed.
This paper introduces LoopCD, a training-free decoding method for looped Transformers that reuses intermediate predictions from earlier recurrent passes to guide token selection. By contrasting predictions from different loop depths, LoopCD improves reasoning accuracy (e.g., AIME scores from 61.88% to 73.33%) while cutting inference compute by 22-48% through fewer required loops.