← Back to all news
arXiv AI By Chanakya Ekbote, Artidoro Pagnoni, Devendra Singh Sachan, Shruti Bhosale, Mike Lewis, Maryam Fazel-Zarandi, Shashank Rajput, Sujan Kumar Gonugondla

How to Loop Your Transformer: Loop Scaling, Specialization, and Parallel Inference

Read the original on arXiv AI →

The paper investigates how looping—reusing transformer weights to deepen models without adding parameters—affects performance under different constraints. Experiments on dense decoder‑only transformers up to 1 B parameters show that looping the full transformer yields best quality for a fixed parameter budget, while looping a middle block is preferable when training FLOPs are limited. Adding loop‑specific low‑rank adapters improves quality, but decoding slows linearly with loops; a parallel loop design that passes hidden states across token positions can mitigate this slowdown, though careful handling of token embeddings is needed to retain gains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Sep 25

FlashLoop: Fast and Memory-Efficient Looped Transformers via Lazy Updates

FlashLoop is a training‑free inference framework for Looped Transformers that reduces cross‑loop redundancy by employing token‑sparse updates, sparse attention, and KV‑residual quantization. It exploits observations that, as loops progress, state changes concentrate on a small token subset, attention differences are dominated by a sparse key subset, and KV residuals become amenable to low‑bit quantization. The method achieves lossless accuracy with up to 1.64× speedup and 6× KV‑cache memory reduction across several Looped Transformer models.

By Wanqi Yang, Shiwei Liu
llmsefficiency
More like this →
arXiv AI
Oct 2

Looping Beyond Twice: A Scalable Recipe for Looped Mixture-of-Experts

The paper introduces LOOM, a method for scaling looped mixture‑of‑experts (MoE) Transformers beyond the typical two‑loop limit. LOOM addresses two key obstacles: it stabilizes deep recurrence by bounding residual variance and re‑injecting the input embedding, and it prevents expert selection collapse by using per‑loop routers and a looping residual to maintain computational diversity. Experiments on 100 M–1.7 B parameter models show stable scaling to 9–12 loops, with significant perplexity reductions and zero‑shot accuracy gains under near‑iso‑FLOP conditions.

By Di He, Pengxiang Li, Da Chang, Qingyan Meng, Lu Yin, Shiwei Liu
llmsrag
More like this →