arxiv
PublishedOctober 12, 2026 at 4:00 AM
Towards Easier-to-Tune Training Algorithms: New Results from AlgoPerf
Publisher summary· verbatim
arXiv:2610.13036v1 Announce Type: new Abstract: Evaluating deep learning optimizer is challenging because confounding factors such as hyperparameter tuning and optimizer-model co-design often obscure true algorithmic performance. We present results of the updated AlgoPerf V1 benchmark, in which we e
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
arxivSmolWAM: A Latent World-Action Model for Robotic Manipulation on Edge Devices15harxivVETR: Execution-Grounded Self-Improving Agent for Symbolic Graphics Reasoning15harxivAI-Based On-Board Maritime Object Detection for Earth Observation Payload Data Reduction on Versal Embedded Hardware15harxivSEEDS: Preview-Guided Evidence Admission for Search Agents15hThe 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 ↗