·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
America needs to stop getting shocked by Chinese AI2h◆Advancing next-gen AI with materials science innovation3h◆Gritt exits stealth with $34 million for robots to build solar plants—then, everything else3h◆Capacity and Redundancy Trade-offs in Multi-Task Learning9h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation9h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making9h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection9h◆Supervised Reward Inference9h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization9h◆Is Progressive Disclosure All You Need for Long-Context Agents?9h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability9h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification9h◆Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration9h◆Time-Frequency Consistency Learning for Robust Speech Deepfake Detection9h◆Scientific reasoning does not reliably translate into scientific forecasting in frontier AI9h◆Language Triggers Hijack Language Circuits: A Mechanistic Analysis of Backdoor Behaviors in Large Language Models9h◆Kernel Regression with Tensor Trains and Hadamard Overparameterization9h◆AI-Augmented Human Resource Management? Insights from German companies9h◆Diagnosing Correctness Probes under Self-Judgement Confounding9h◆BLAD: A Historically Contextualized, Multilingual Dataset of Bangladeshi Legal Acts (1799 to 2025)9h◆America needs to stop getting shocked by Chinese AI2h◆Advancing next-gen AI with materials science innovation3h◆Gritt exits stealth with $34 million for robots to build solar plants—then, everything else3h◆Capacity and Redundancy Trade-offs in Multi-Task Learning9h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation9h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making9h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection9h◆Supervised Reward Inference9h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization9h◆Is Progressive Disclosure All You Need for Long-Context Agents?9h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability9h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification9h◆Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration9h◆Time-Frequency Consistency Learning for Robust Speech Deepfake Detection9h◆Scientific reasoning does not reliably translate into scientific forecasting in frontier AI9h◆Language Triggers Hijack Language Circuits: A Mechanistic Analysis of Backdoor Behaviors in Large Language Models9h◆Kernel Regression with Tensor Trains and Hadamard Overparameterization9h◆AI-Augmented Human Resource Management? Insights from German companies9h◆Diagnosing Correctness Probes under Self-Judgement Confounding9h◆BLAD: A Historically Contextualized, Multilingual Dataset of Bangladeshi Legal Acts (1799 to 2025)9h◆
News/Are we Merging the Right Models? Impact of Expert Training Duration on Model Merging for LLMs
arxiv
PublishedJuly 15, 2026 at 4:00 AM
—neutral

Are we Merging the Right Models? Impact of Expert Training Duration on Model Merging for LLMs

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

arXiv:2607.11997v1 Announce Type: cross Abstract: Multi-task model merging combines separately trained expert models into a single model that handles all tasks without co-training. Standard practice merges experts at their optimal validation loss. We challenge this convention by systematically study

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
arxivCapacity and Redundancy Trade-offs in Multi-Task Learning9harxivPredictive Training with Latent Imagination for Visual Quadruped Navigation9harxivWhere Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making9harxivDid We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection9h
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