·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation2h◆Counterfactual Model Testing Under Distribution Shift2h◆Centralized Critics for N-Agent Ad Hoc Teamwork2h◆Where and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning2h◆Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction2h◆Numina Fuse: Interactive Autoformalization in Lean 42h◆On the Limits of Sparse Linear Model with Shuffled Differential Privacy2h◆Video World Models Need Predictable Latents2h◆AutoPRL: Automated Path Representation Learning via a Learnable View Generator2h◆Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models2h◆Latent Goal Prediction from Language for Model-Based Planning2h◆Rectified Language Flow2h◆Trainable Boundary-Keeping Where No Formal Answer Exists: A Controlled Knock-In Study of Reward Training in Evidence-Sufficiency Judgment2h◆Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila2h◆Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights2h◆Intervention-Certified Amplification: A Validity Gate for $\sigma$-Weighted Post-Training2h◆Single-Cycle Battery Remaining Useful Life Prediction with Compressed Continuous Wavelet Features and Deep Neural Networks2h◆DoubleFree:Exploiting Exploration History for Rollout-Free Multi-Task LLMs Training2h◆Unbiased Alignment for Large Language Models with Noisy Preferences2h◆ExCal: Optimization-Free Multivariate Time Series Anomaly Detection via Excess Calibration2h◆MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation2h◆Counterfactual Model Testing Under Distribution Shift2h◆Centralized Critics for N-Agent Ad Hoc Teamwork2h◆Where and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning2h◆Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction2h◆Numina Fuse: Interactive Autoformalization in Lean 42h◆On the Limits of Sparse Linear Model with Shuffled Differential Privacy2h◆Video World Models Need Predictable Latents2h◆AutoPRL: Automated Path Representation Learning via a Learnable View Generator2h◆Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models2h◆Latent Goal Prediction from Language for Model-Based Planning2h◆Rectified Language Flow2h◆Trainable Boundary-Keeping Where No Formal Answer Exists: A Controlled Knock-In Study of Reward Training in Evidence-Sufficiency Judgment2h◆Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila2h◆Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights2h◆Intervention-Certified Amplification: A Validity Gate for $\sigma$-Weighted Post-Training2h◆Single-Cycle Battery Remaining Useful Life Prediction with Compressed Continuous Wavelet Features and Deep Neural Networks2h◆DoubleFree:Exploiting Exploration History for Rollout-Free Multi-Task LLMs Training2h◆Unbiased Alignment for Large Language Models with Noisy Preferences2h◆ExCal: Optimization-Free Multivariate Time Series Anomaly Detection via Excess Calibration2h◆
News/Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models
arxiv
PublishedOctober 28, 2026 at 4:00 AM

Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models

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

arXiv:2610.35804v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) can generate multiple tokens in parallel, but often require many decoding rounds to complete a response. Efficient generation depends not only on which tokens are easy to predict, but also on how committing to

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
arxivMAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation2harxivCounterfactual Model Testing Under Distribution Shift2harxivCentralized Critics for N-Agent Ad Hoc Teamwork2harxivWhere and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning2h
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