arxiv
PublishedApril 13, 2026 at 4:00 AM
Is More Data Worth the Cost? Dataset Scaling Laws in a Tiny Attention-Only Decoder
Publisher summary· verbatim
arXiv:2604.09389v1 Announce Type: cross Abstract: Training Transformer language models is expensive, as performance typically improves with increasing dataset size and computational budget. Although scaling laws describe this trend at large scale, their implications in controlled, smaller-scale sett
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response11harxivLearning Transferable Latent User Preferences for Human-Aligned Decision Making11harxivRepair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents11harxivIndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD11hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗