I am a CS PhD candidate (2023-) at Dartmouth College. I earned my Master's degree in EECS at UC Berkeley.
Previously, I interned at Salesforce AI Research.

Research:  My research focuses on developing evaluation metrics and systems for ML models, spanning three dimensions: training quality, output reliability and hyperparameter. This research contributes to
  • Efficient training:  LLM fine-tuning [3, 8], hyperparameter optimization [6]
  • Auto-evaluation:  LLM verification for reasoning [1]
  • Inference optimization:  pruning [4, 7], parallel decoding [2], ensembling [5]

News

SF Logo Jun 2025 - Sep 2025: I joined Salesforce AI Research as a Research Intern, check out our work on auto-evaluation.

📣 Aug 2024: I passed the PhD qualification exam!

Recent Publications

* indicates equal contribution
[1] Variation in Verification: Understanding Verification Dynamics in Large Language Models
Yefan Zhou, Austin Xu, Yilun Zhou, Janvijay Singh, Jiang Gui, Shafiq Joty

Preprint
Paper / Project / Twitter

[auto-evaluation, judge for reasoning, test-time scaling]

[2] Diffusion Language Models Know the Answer Before Decoding
Pengxiang Li, Yefan Zhou, Dilxat Muhtar, Lu Yin, Shilin Yan, Li Shen, Yi Liang,
Soroush Vosoughi, Shiwei Liu

Preprint
Paper / Code

[efficient inference, parallel decoding, DLM]

[3] Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training
{Yefan Zhou*, Tianyu Pang*}, Keqin Liu, Charles H. Martin, Michael Mahoney, Yaoqing Yang

NeurIPS 2023 Spotlight
Paper / Code / Video

[efficient training, NN optimizer, weight/layer analysis]

[4] AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
{Haiquan Lu*, Yefan Zhou*}, Shiwei Liu, Zhangyang Wang, Michael W. Mahoney, Yaoqing Yang

NeurIPS 2024
Paper / Code

[efficient inference, LLM pruning, weight/layer analysis]

[5] Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
{Haiquan Lu*, Xiaotian Liu*, Yefan Zhou*, Qunli Li*}, Kurt Keutzer, Michael W. Mahoney, Yujun Yan, Huanrui Yang, Yaoqing Yang

NeurIPS 2024
Paper / Code

[Ensembling, Data selection, OOD]


[6] MD tree: a model-diagnostic tree grown on loss landscape
{Yefan Zhou*, Jianlong Chen*}, Qinxue Cao, Konstantin Schürholt, Yaoqing Yang

ICML 2024
Paper / Code / Video

[Scaling law, Hyperparameter tuning for training]


[7] A Three-regime model of Network Pruning
Yefan Zhou, Yaoqing Yang, Arin Chang, Michael Mahoney

ICML 2023
Paper / Code / Video

[NN pruning, efficient inference]


[8] Model Balancing Helps Low-data Training and Fine-tuning
Zihang Liu, Yuanzhe Hu, Tianyu Pang, Yefan Zhou, Pu Ren, Yaoqing Yang

EMNLP main 2024 Oral
Paper / Code

[LLM fine-tuning, Layer quality analysis]


AlphaExpert: Assigning LoRA Experts Based on Layer Training Quality
Peijun Qing, Chongyang Gao, Yefan Zhou, Xingjian Diao, Pu Ren, Yaoqing Yang, Soroush Vosoughi

EMNLP main 2024
Paper

[LLM efficient fine-tuning, Mixture-of-expert]


Work Experiences

  • Jun 2025 - Sep 2025: Research Intern @ Salesforce AI Research, working with Austin Xu, Yilun Zhou and Shafiq Joty.
  • Jan 2023 - Aug 2023: Research Intern @ UC Berkeley ICSI and Sky Computing Lab, working with Yaoqing Yang and Michael Mahoney.

Recent Talks

  • Nov. 2024: Talk at Snowflake, "Weight Matrix Diagnostics and Improved Large Language Model Compression and Fine-tuning"
  • Jan. 2024: Talk at AI-TIME, "Phase transition, loss landscape and model diagnostics"
  • Oct. 2023: Talk at UC Berkeley/ICSI TrojAI onsite, "Layer-wise Weight Analysis and Neural Network Training"
  • Mar. 2023: Talk at UC Berkeley/ICSI TrojAI onsite, "A Three-regime model of Network Pruning"

Services

Conference Reviewer

Journal Reviewer

  • Transactions on Machine Learning Research(TMLR)

Last Updated: Oct 2025.

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