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Improving Test-Time Scaling with Adaptive Looped Transformers

Paper recorded by Signals 4 on 2026-09-28 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-28 on arXiv · recorded by Signals 4 on 2026-09-29

Category: cs.LG · 机器学习 · first seen 2026-09-29

Abstract

Looped transformers have demonstrated promising parameter efficiency by reusing layers for latent computation. Prior studies compare looped and non-looped models at matched parameters or per-token FLOPs. However, to the best of our knowledge, whether looping improves test-time scaling as outputs grow longer remains underexplored. Through post-training looped transformers, we study the accuracy-com

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#5 most recent of 322 cs.LG papers we have recorded · ↑ newer: Neural Harmonic Measure Operator · ↓ older: Harness Learning Enables Generalizable Test-Time Adaptation
Cite this page: Improving Test-Time Scaling with Adaptive Looped Transformers: the #5 most recent of 322 cs.LG papers we have recorded (as of 2026-09-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/improving-test-time-scaling-with-adaptive-looped-transformers.html
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