General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization
No confident code implementation found for this paper. 1 possible match can be shown below.
No confident code implementation found for this paper. 1 possible match can be shown below.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization
Improving Online-to-Nonconvex Conversion for Smooth Optimization via Double Optimism
Meta-Black-Box-Optimization through Offline Q-function Learning
A Nonstochastic Control Approach to Optimization
Analysis of Schedule-Free Nonconvex Optimization
From SWE-ZERO to SWE-HERO: Execution-free to Execution-based Fine-tuning for Software Engineering Agents
@article{ahn2026general,
title = {General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization},
author = {Kwangjun Ahn and Gagik Magakyan and Ashok Cutkosky},
year = {2026},
url = {https://api.semanticscholar.org/CorpusID:4fc176d2f12b472ab08bde93383e043ef6a4c11d},
journal = {ICML 2025 2025}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/4fc176d2f12b472ab08bde93383e043ef6a4c11d)