← Back to all news
Hugging Face Trending Papers November 3, 2026

Best-of-Depth: Learning Exploratory Dynamics in Looped Transformers

Read the original on Hugging Face Trending Papers →

The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.

  • llms
  • fine-tuning

One email a morning, machine-written

One email a day, machine-written, one click to leave. We never share your address.

Related stories

arXiv AI
Aug 12

Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers

arXiv:2604. 07822v2 Announce Type: replace-cross Abstract: We study implicit reasoning, i.

By Harsh Kohli, Srinivasan Parthasarathy, Huan Sun, Yuekun Yao
llms
More like this →
arXiv Computation and Language
Sep 30

Pretraining Latent Information Feedback Transformers with Teacher Supervision

arXiv:2609.38149v1 Announce Type: new Abstract: Transformer language models (LMs) are feed-forward: deep-layer representations are never fed back to shallower layers, and the only pathway for informa...

By Dor Tirosh, Ido Amos, Mor Geva
llms
More like this →
arXiv Computation and Language
Oct 26

Accordion: Elastic-Depth Inference via Fixed-Prefix Layer Looping

arXiv:2610.32492v1 Announce Type: cross Abstract: Large language models serve applications with diverse inference budgets, but training and maintaining separate models for each budget incurs substant...

By Yudong Zhang, Wei Ding, Kaili Zheng, Yuchen Ding, Zhenyu Zhang
llmsreinforcement-learningfine-tuningefficiency
More like this →
arXiv Machine Learning
Aug 18

RecurrentGPT: Expressive Depth through Recurrent Modulation in Transformers

arXiv:2608. 15062v1 Announce Type: cross Abstract: Scaling transformer language models creates an inherent tension between expressivity and memory efficiency.

By Amr Hegazy, Amr Alanwar, Mostafa Elhoushi
llmsefficiency
More like this →
arXiv AI
Sep 16

Thinking Deeper, Not Longer: Memory-Efficient Test-Time Reasoning with Depth-Recurrent Transformers for Compositional Generalization

arXiv:2603.21676v2 Announce Type: replace-cross Abstract: Standard Transformers have a fixed computational depth, limiting their ability to generalize to tasks that require variable-depth reasoning....

By Hung-Hsuan Chen
llmssafety
More like this →
arXiv Machine Learning
Sep 30

What Makes Recurrence Effective in Looped Language Models?

arXiv:2609.36636v1 Announce Type: new Abstract: Looped language models (LoopLMs) increase computational depth through parameter sharing, offering a path to scale inference computation without adding...

By Xinlin Zhuang, Siyuan Wang, Imran Razzak, Weiyang Liu
llms
More like this →
About Pricing API Newsletter Sources Privacy Terms Refunds Accessibility Provider info Contact RSS

The Flow links to publishers and never republishes their articles. Summaries are machine-generated.

v1.1.0 · 5f852ea