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Decoding Looped Transformers Better for (Almost) Free

Paper recorded by Signals 4 on 2026-10-01 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-01 on arXiv · recorded by Signals 4 on 2026-10-02

Category: cs.LG · 机器学习 · first seen 2026-10-02

Abstract

Looped Transformers achieve parameter efficiency by repeatedly executing a shared block across recurrent loops. Each loop yields an intermediate representation decodable for the same next token, yet standard decoding discards earlier states. Because earlier loops embody less computation, recurrence inherently supplies aligned weak-and-strong prediction pairs without auxiliary models or external tr

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#7 most recent of 362 cs.LG papers we have recorded · ↑ newer: Generative modeling of intrinsically disordered protein regions by rei · ↓ older: From Gradients to Capabilities: Understanding Multi-Teacher On-Policy
Cite this page: Decoding Looped Transformers Better for (Almost) Free: the #7 most recent of 362 cs.LG papers we have recorded (as of 2026-10-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/decoding-looped-transformers-better-for-almost-free.html
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