# LoopCD

The work introduces LoopCD, a training-free contrastive decoding framework that guides token selection by contrasting a looped Transformer's final prediction with an earlier recurrent pass, in either logit space with one extra output pass (LoopCD-Logits) or hidden-state space with zero output overhead (LoopCD-Hidden); across four looped Transformer families it delivers consistent gains at full recurrent depth, for example raising Ouro-2.6B-Thinking's AIME 2024 pass@1 from 61.88% to 73.33% and Huginn's HumanEval pass@1 from 22.56% to 31.71%, and these gains let it match or exceed full-depth unguided baselines at half the recurrent loops while reducing forward FLOPs by 22.5% to 48.2%.

The work introduces LoopCD, a training-free contrastive decoding framework that guides token selection by contrasting a looped Transformer's final prediction with an earlier recurrent pass, in either logit space with one extra output pass (LoopCD-Logits) or hidden-state space with zero output overhead (LoopCD-Hidden); across four looped Transformer families it delivers consistent gains at full recurrent depth, for example raising Ouro-2.6B-Thinking's AIME 2024 pass@1 from 61.88% to 73.33% and Huginn's HumanEval pass@1 from 22.56% to 31.71%, and these gains let it match or exceed full-depth unguided baselines at half the recurrent loops while reducing forward FLOPs by 22.5% to 48.2%.