·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures3h◆The Answer Lies Within: Self-Derived Rewards Enable Explainable Relation Extraction3h◆Protecting De-identified Documents from Search-based Linkage Attacks3h◆ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation3h◆RayCo4D: Ray-Conditioned Continuous Motion Model for Monocular 4D Dynamic Reconstruction3h◆Orbitol: Crystal Generation on Wyckoff Orbits3h◆VLA-Rolling: Fewer Denoising Steps, Better Control via Adaptive Action Reuse3h◆How to Use Parallel Data for Multilingual Pretraining3h◆Differentially Private Sparse Reward Estimation with Preference Feedback3h◆KIRA: A Full-Stack Reading Screening System with Dual-Pathway Analysis for Children3h◆Kinetic theory for Transformers and the lost-in-the-middle phenomenon3h◆Building AI Research Teams for Autonomous Discovery of Linear Attention Architectures3h◆CSD: Common-Support Distillation for Cross-Tokenizer Knowledge Transfer3h◆Is Refusal Direction Universal across Low-Resourced Languages?3h◆Guilt by Association: Semantic Leakage Drives Associative Gender Bias3h◆Scan or Index? large Evidence Coverage, Answer Quality, and Cost in Long-Document RAG3h◆Language Impairment as a Lens onto Large Language Models3h◆Target Regeneration Shrinks the Gains of Draft-Training Objectives in Speculative Decoding3h◆Don't Stop Long-Context Continual Pre-Training Too Early: An Empirical Scaling Law3h◆Right On Q: Recovering Reasoning under Quantization via Self-Generated Trajectories3h◆Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures3h◆The Answer Lies Within: Self-Derived Rewards Enable Explainable Relation Extraction3h◆Protecting De-identified Documents from Search-based Linkage Attacks3h◆ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation3h◆RayCo4D: Ray-Conditioned Continuous Motion Model for Monocular 4D Dynamic Reconstruction3h◆Orbitol: Crystal Generation on Wyckoff Orbits3h◆VLA-Rolling: Fewer Denoising Steps, Better Control via Adaptive Action Reuse3h◆How to Use Parallel Data for Multilingual Pretraining3h◆Differentially Private Sparse Reward Estimation with Preference Feedback3h◆KIRA: A Full-Stack Reading Screening System with Dual-Pathway Analysis for Children3h◆Kinetic theory for Transformers and the lost-in-the-middle phenomenon3h◆Building AI Research Teams for Autonomous Discovery of Linear Attention Architectures3h◆CSD: Common-Support Distillation for Cross-Tokenizer Knowledge Transfer3h◆Is Refusal Direction Universal across Low-Resourced Languages?3h◆Guilt by Association: Semantic Leakage Drives Associative Gender Bias3h◆Scan or Index? large Evidence Coverage, Answer Quality, and Cost in Long-Document RAG3h◆Language Impairment as a Lens onto Large Language Models3h◆Target Regeneration Shrinks the Gains of Draft-Training Objectives in Speculative Decoding3h◆Don't Stop Long-Context Continual Pre-Training Too Early: An Empirical Scaling Law3h◆Right On Q: Recovering Reasoning under Quantization via Self-Generated Trajectories3h◆
News/Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference
arxiv
PublishedSeptember 7, 2026 at 4:00 AM
—neutral

Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2609.05275v1 Announce Type: new Abstract: Layer dropout (a.k.a. stochastic depth) has been shown to enable faster training, higher accuracy, and robustness to zero-shot layer pruning in both language and vision transformers. However, as models and datasets have scaled, dropout - particularly l

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivHyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures3harxivThe Answer Lies Within: Self-Derived Rewards Enable Explainable Relation Extraction3harxivProtecting De-identified Documents from Search-based Linkage Attacks3harxivClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation3h
The Bubble Brief
MON · WED · FRI

Read AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews