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LoopCD improves looped-transformer scores while cutting recurrent compute

huggingface.co by Weihao Liu et al. Editor score 9/10

Training-free LoopCD raises Ouro-2.6B-Thinking AIME 2024 pass@1 from 61.88% to 73.33% and cuts forward FLOPs by 22.5–48.2% when fewer loops suffice. It turns intermediate recurrent predictions into a decoding signal that can improve quality and reduce inference compute.

What
LoopCD is a training-free contrastive decoding framework for looped Transformers.
Method
It contrasts the final prediction with an earlier recurrent pass, using logits or hidden states to guide token selection.
Why it matters
It can halve recurrent loops while matching or exceeding full-depth unguided baselines, cutting forward FLOPs by 22.5%–48.2%.
recurrent pass final pass contrast contrast shared block earlierrecurrent pass final prediction token selection
LoopCD contrasts predictions from earlier and final recurrent passes to guide token selection. Sketched from the article's own text.

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