HMoRA: Making LLMs More Effective with Hierarchical Mixture of LoRA Experts

Mengqi Liao, Wei Chen, Junfeng Shen, S. Guo, Huaiyu Wan
2/12/2026

No confident code implementation found for this paper. 1 possible match can be shown below.

Semantic Scholar

Code Implementations

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!

Similar Papers

Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification

98% match2026

Occluded Gait Recognition with Mixture of Experts: An Action Detection Perspective

97% match1 repo92026

Towards Theoretical Understanding of Sequential Decision Making with Preference Feedback

97% match2026

Unified Generative Modeling of 3D Molecules with Bayesian Flow Networks

97% match1 repo592026

Benign Oscillation of Stochastic Gradient Descent with Large Learning Rate

97% match2026

Generalization Analysis of Stochastic Weight Averaging with General Sampling

97% match2026

Cite this paper

@article{liao2026hmora,
  title  = {HMoRA: Making LLMs More Effective with Hierarchical Mixture of LoRA Experts},
  author = {Mengqi Liao and Wei Chen and Junfeng Shen and S. Guo and Huaiyu Wan},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:372e8996249f6c1d04bf300123ba8efda7af1f28},
  journal = {ICLR 2025 2025}
}

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/372e8996249f6c1d04bf300123ba8efda7af1f28.svg)](https://codeofpaper.com/paper/372e8996249f6c1d04bf300123ba8efda7af1f28)

Discussion