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News/Efficient Long-Horizon Learning for Learned Optimization
arxiv
PublishedJuly 10, 2026 at 4:00 AM
▲bullish

Efficient Long-Horizon Learning for Learned Optimization

Source
arxiv.orgfull article ↗
Read on arxiv→
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
  • 01AI organization logo
    gpt2
    gpt2
  • 02google logo
    vit-base-patch16-224-in21k
    google/vit-base-patch16-224-in21k
  • 03microsoft logo
    resnet-50
    microsoft/resnet-50
Compare these 3 models→
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Discussion
Mentioned models
06
  • 01
    Adam
  • 02
    Muon
  • 03
    gpt2
    gpt2
  • 04
    gpt2
    gpt2
  • 05
    vit-base-patch16-224-in21k
    google/vit-base-patch16-224-in21k
  • 06
    resnet-50
    microsoft/resnet-50
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#optimization#meta-learning#language-modeling#image-classification
Mentioned companies
01
Meta

No replies yet. Be first.

Mentioned models
06
  • 01
    Adam
  • 02
    Muon
  • 03
    gpt2
    gpt2
  • 04
    gpt2
    gpt2
  • 05
    vit-base-patch16-224-in21k
    google/vit-base-patch16-224-in21k
  • 06
    resnet-50
    microsoft/resnet-50
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#optimization#meta-learning#language-modeling#image-classification
Mentioned companies
01
Meta

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