🧑‍🔬 Jipeng Zhang

🏛️ Affiliations

🎓Hong Kong University of Science and TechnologyHong Kong · Hong Kong2021–2026 · 64 papersEducation 2021–2026 · PhD, CSE🎓University of Electronic Science and Technology of ChinaChengdu · China2019–2020 · 5 papers🎓University of Illinois Urbana-ChampaignUrbana · United States2023–2025 · 3 papers🎓Wuhan UniversityWuhan · China2024 · 2 papers🎓Singapore Management UniversitySingapore · Singapore2020 · 2 papers🏢MicrosoftSingapore · SingaporeEmployment

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

🧭 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment (2023 · 239 citations)
   Aligning generative foundation models with human preferences usually relies on RLHF with PPO. PPO is unstable and sample-inefficient, and it must keep several large models in memory at once (policy, reference, reward, critic). The authors propose RAFT (Reward rAnked FineTuning), a simpler alternative that repeats…
2. Mitigating the Alignment Tax of RLHF (2024 · 82 citations)
   RLHF makes LLMs follow human preferences, but it also tends to erode skills learned in pre-training. This loss is called the alignment tax. The authors measured it by aligning OpenLLaMA-3B on the Anthropic helpfulness/harmlessness data with Rejection Sampling Fine-tuning (RSF) and DPO. They then tracked performance…

All works

8 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. RAFT: Reward rAnked FineTuning for Generative Foundation Model AlignmentarXiv:2304.06767Hanze Dong, Wei Xiong, Deepanshu Goyal, et al.2023 · 239 citations
2. Mitigating the Alignment Tax of RLHFConference on Empirical Methods in Natural Language Processing (EMNLP)Yong Lin, Hangyu Lin, Wei Xiong, et al.2024 · 82 citations
3. G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language ModelarXiv:2312.11370 · original ↗ · no summary yetJiahui Gao et al.2023 · 74 citations
4. MLLM-Protector: Ensuring MLLM's Safety without Hurting PerformanceProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · original ↗ · no summary yetRenjie Pi et al.2024 · 57 citations
5. TheoremLlama: Transforming General-Purpose LLMs into Lean4 ExpertsarXiv:2407.03203 · original ↗ · no summary yetRuida Wang et al.2024 · 50 citations
6. Process-Driven Autoformalization in Lean 4arXiv:2406.01940 · original ↗ · no summary yetJianqiao Lu et al.2024 · 33 citations
7. MA-LoT: Model-Collaboration Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem ProvingarXiv:2503.03205 · original ↗ · no summary yetRuida Wang et al.2025 · 17 citations
8. LongVideoAgent: Multi-Agent Reasoning with Long VideosProceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetRuntao Liu et al.2026 · 3 citations

🤝 Co-authors here

9 co-authors with a page, the most papers together first.

• Hanze Dong ×2
• Rui Pan ×2
• Shizhe Diao ×2
• Wei Xiong ×2
• Hangyu Lin
• Haoxiang Wang
• Kashun Shum
• Tong Zhang
• Yong Lin

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Conference on Empirical Methods in Natural Language Processing (EMNLP) ×2: EMNLP 2024
• Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

🔗 Cited by

Papers of our library cite their works 445 times (of the 141,840 papers whose bibliographies we read); 33 citing papers with a page here →.

Updated 24 October 2026

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