Mini-Sequence Transformers: Optimizing Intermediate Memory for Long Sequences Training

Cheng Luo, Jiawei Zhao, Zhuoming Chen, Beidi Chen, Anima Anandkumar
2/3/2026

No confident code implementation found for this paper.

Semantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{luo2026minisequence,
  title  = {Mini-Sequence Transformers: Optimizing Intermediate Memory for Long Sequences Training},
  author = {Cheng Luo and Jiawei Zhao and Zhuoming Chen and Beidi Chen and Anima Anandkumar},
  year   = {2026},
  url    = {https://www.semanticscholar.org/paper/67a825615f9165475d0b17ad8abb47a098a43444},
  journal = {NEURIPS 2024 2024}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/67a825615f9165475d0b17ad8abb47a098a43444.svg)](https://codeofpaper.com/paper/67a825615f9165475d0b17ad8abb47a098a43444)

Discussion