·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
AI has a water problem. Google thinks it has a fix1h◆Google must let publishers opt out of AI Search features, rules UK1h◆FederatedSkill: Federated Learning for Agentic Skill Evolution6h◆Toward a Modular Architecture for Embedded AI Agent Systems at the Edge6h◆A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation6h◆Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate6h◆Evaluating the Reversal Curse in Model Editing6h◆Fast Unlearning at Scale via Margin Self-Correction6h◆Can Local Learning Match Self-Supervised Backpropagation?6h◆CAPER: Clause-Aligned Process Supervision for Text-to-SQL6h◆An Asymptotic Theory of Chain-of-Thought in In-Context Learning6h◆DeskCraft: Benchmarking Desktop Agents on Professional Workflows and Human-in-the-Loop Collaboration6h◆Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction6h◆Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems6h◆Closed-Loop Molecular Design with Calibrated Deference6h◆Latent Laplace Diffusion for Irregular Multivariate Time Series6h◆Coherence Maximization Improves Pluralistic Alignment6h◆Language Bias under Conflicting Information in Multilingual LLMs6h◆DFlare: Scaling Up Draft Capacity for Block Diffusion Speculative Decoding6h◆ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?6h◆AI has a water problem. Google thinks it has a fix1h◆Google must let publishers opt out of AI Search features, rules UK1h◆FederatedSkill: Federated Learning for Agentic Skill Evolution6h◆Toward a Modular Architecture for Embedded AI Agent Systems at the Edge6h◆A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation6h◆Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate6h◆Evaluating the Reversal Curse in Model Editing6h◆Fast Unlearning at Scale via Margin Self-Correction6h◆Can Local Learning Match Self-Supervised Backpropagation?6h◆CAPER: Clause-Aligned Process Supervision for Text-to-SQL6h◆An Asymptotic Theory of Chain-of-Thought in In-Context Learning6h◆DeskCraft: Benchmarking Desktop Agents on Professional Workflows and Human-in-the-Loop Collaboration6h◆Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction6h◆Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems6h◆Closed-Loop Molecular Design with Calibrated Deference6h◆Latent Laplace Diffusion for Irregular Multivariate Time Series6h◆Coherence Maximization Improves Pluralistic Alignment6h◆Language Bias under Conflicting Information in Multilingual LLMs6h◆DFlare: Scaling Up Draft Capacity for Block Diffusion Speculative Decoding6h◆ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?6h◆
News/When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
arxiv
PublishedJune 2, 2026 at 4:00 AM
—neutral

When Data Is Scarce: Scaling Sparse Language Models with Repeated Training

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

arXiv:2606.01155v1 Announce Type: cross Abstract: Scaling laws for dense LLMs under infinite data are well explored, but how sparsity interacts with limited data is not. In this work, we study sparse training in data-constrained regimes where limited unique tokens require multi-epoch training. Our e

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

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

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
arxivFederatedSkill: Federated Learning for Agentic Skill Evolution6harxivToward a Modular Architecture for Embedded AI Agent Systems at the Edge6harxivA Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation6harxivAnomalies in Multivariate Time Series Benchmarks Are Mostly Univariate6h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

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

Originally published on arxiv ↗
HomeModelsNews