avatar

Runming Yang
杨润明

Ph.D. student at the University of Hong Kong
yang3121099@126.com

Biography

I am a Ph.D. student in Electrical and Electronic Engineering at the University of Hong Kong, supervised by Prof. Ngai Wong. I received my M.Eng. in Big Data Engineering from Tsinghua University in 2025, advised by Prof. Yujiu Yang, and my B.Eng. from Huazhong University of Science and Technology in 2021.

View my academic CV (PDF)   ·   Download PDF

Research Interests

Efficient LLM post-training; Knowledge distillation; Low-bit quantization; Efficient reasoning. My work explores model compression and efficient reasoning in large language models.

Selected Publications

Selected published and accepted papers. Full publications and preprints (PDF).

* indicates equal contribution.

Industry Experience

Tencent PCG — Algorithm Intern, Social Secure Center
Nov. 2023 - Jun. 2025

  • Built domain-specific multimodal datasets for model development and evaluation.
  • Conducted model post-training and evaluation on domain-specific tasks.
  • Investigated cross-modal feature fusion for multimodal modeling.

Projects

Project 1 Image

LoCa (ECAI 24')

  • Identify that misclassification by the teacher during distillation harms student learning.
  • Propose LoCa, calibrating the logits distribution of misclassified samples to ensure target label accuracy.
  • Conduct experiments on image classification and natural language generation tasks; easily extendable to LLMs.
  • Improve accuracy while adding only 1% computational cost as an enhanced plug-in.

    • arxiv
    Project 2 Image

    LLM-Neo

  • Discover similarities between Knowledge Distillation and Low-rank Adapter methods in knowledge transfer for LLMs.
  • Combine both ideas and create a more efficient knowledge transfer strategy, LLM-Neo.
  • Demonstrate better performance on the Llama 3.1 series compared to SFT, LoRA, and KD

    • huggingface
    Project 2 Image

    Kaggle (🥈Silver Medal)

  • Aim to assess juniors' English writing proficiency, which is few-samples and long-text.
  • Utilize techniques like data augmentation, token truncation and dropout regularization.
  • Achieve a silver medal via 8 models (i.e., RoBERTa) ensembled and 5-fold validation.

    • kaggle
    Project 2 Image

    Need for Speed (HUST Undergraduate Course Design)

  • A copy of "Need for Speed" game using pure C language.
  • Pixel style, retro sentiment
  • Implemente key game mechanics, i.e., acceleration, turning, drifting, and collision handling.

    • (Last updated: 2026-10-13)




    One more thing that excites me is meeting the gradient-color cloud and lonely tree. I'd like to capture them and share them with others!