arxiv
PublishedJuly 10, 2026 at 4:00 AM
▲bullish
Efficient Long-Horizon Learning for Learned Optimization
Publisher summary· verbatim
arXiv:2607.06772v2 Announce Type: replace Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.g., Adam and Muon) by meta-learning small neural network optimizers over a distribution of tasks. While recent work has greatly advanced the architectural design and inductive bi
Models mentioned
03
Related
05- arxivJun 2How Much Orthogonalization Does Muon Need?
- arxivMay 1Making Logic a First-Class Citizen in Generative ML for Networking
- arxivApr 20Adapting in the Dark: Efficient and Stable Test-Time Adaptation for Black-Box Models
- arxivApr 10Information as Structural Alignment: A Dynamical Theory of Continual Learning
- arxivApr 9STQuant: Spatio-Temporal Adaptive Framework for Optimizer Quantization in Large Multimodal Model Training
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivThe Steering Budget: Examples beat Knobs3harxivPolestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs3harxivRxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination3harxivWhen a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models3hThe Bubble Brief
WEEKLYRead optimization insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗