--- title: "Renjie Pi — author profile and papers — Elamur Research" description: "Renjie Pi, Hong Kong University of Science and Technology: 6 papers on Elamur Research (2024–2026), h-index 14 — affiliations, topics…" canonical: "https://research.elamur.ai/free/en/authors/renjie-pi/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/renjie-pi/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Renjie Pi # Renjie Pi Renjie Pi has 6 papers in our library, in 2024–2026, most of them on [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/); often with [Jipeng Zhang](https://md.research.elamur.ai/free/en/authors/jipeng-zhang/), [Rui Pan](https://md.research.elamur.ai/free/en/authors/rui-pan/), [Hanze Dong](https://md.research.elamur.ai/free/en/authors/hanze-dong/). 6 papers in our library · 2024–2026 [OpenAlex](https://openalex.org/A5028182071) · [ORCID](https://orcid.org/0000-0002-5033-7096) · [**h-index 14**](https://openalex.org/authors/A5028182071) · [70 works](https://openalex.org/works?filter=author.id:A5028182071) · [552 citations](https://openalex.org/works?filter=author.id:A5028182071&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/renjie-pi/#papers) · [🔗 Cited 258 times in our library](https://md.research.elamur.ai/free/en/authors/renjie-pi/cited-by/) · [3 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #5779](https://md.research.elamur.ai/free/en/paper-stars/page/58/#star-5779) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Renjie+Pi) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Renjie+Pi) · [dblp](https://dblp.org/search?q=Renjie+Pi) · [ACL Anthology](https://aclanthology.org/search/?q=Renjie+Pi) ## Affiliations [🎓**Hong Kong University of Science and Technology** Hong Kong · China 2021–2026 · 53 papersEducation*current*](https://md.research.elamur.ai/free/en/universities/hong-kong-university-of-science-and-technology/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 8 The field of science this work belongs to (OpenAlex fields).[Computer Science · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 5 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Computational Theory and Mathematics · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computational-theory-and-mathematics/) · [Social Sciences · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Sociology and Political Science · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) 🧭 Topics 4 Research subjects and languages the work is about.[Bias, fairness & safety · 1 work](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · [Reasoning · 2 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/renjie-pi/year/2026/) · [**2025** 2 papers](https://md.research.elamur.ai/free/en/authors/renjie-pi/year/2025/) · [**2024** 2 papers](https://md.research.elamur.ai/free/en/authors/renjie-pi/year/2024/) ## Papers The most cited first (OpenAlex and our library). 1. **[Mitigating the Alignment Tax of RLHF](https://md.research.elamur.ai/free/en/papers/lin-2024-mitigating-alignment-tax-rlhf/)** *(2024 · 83 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… 2. **[MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance](https://md.research.elamur.ai/free/en/papers/pi-2024-mllm-protector-ensuring-mllm-s/)** *(2024 · 58 citations)* Multimodal large language models such as LLaVA can be steered into harmful output by malicious images, even when the same request in plain text would be refused. 3. **[MA-LoT: Model-Collaboration Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem Proving](https://md.research.elamur.ai/free/en/papers/wang-2025-ma-lot-model-collaboration-lean/)** *(2025 · 18 citations)* The paper addresses automated theorem proving in Lean4. Current LLM approaches fall into two camps. Step-by-step tree search uses verifier feedback but is costly and lacks high-level planning. Whole-proof generation exploits natural-language reasoning but never sees intermediate verifier signals. MA-LoT bridges these… 4. **[Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation](https://md.research.elamur.ai/free/en/papers/yang-2026-nemotron-cascade-2-post-training/)** *(2026 · 16 citations)* Post-training a single LLM across many reasoning and agentic domains is hard. Mixing diverse RL environments can destabilize training, and gains in one area often erode others. Nemotron-Cascade 2 extends NVIDIA's earlier Cascade RL approach, which trains domain by domain in sequence, to address this. The base model… 5. **[VLM2-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues](https://md.research.elamur.ai/free/en/papers/zhang-2025-vlm2-bench-closer-look-how/)** *(2025 · 12 citations)* The paper asks whether vision-language models can do something people do routinely: recognise that the same object or person appears in several images or video frames by comparing visual traits, without knowing who or what it is. 6. **[LongVideoAgent: Multi-Agent Reasoning with Long Videos](https://md.research.elamur.ai/free/en/papers/liu-2026-longvideoagent-multi-agent-reasoning-long/)** *(2026 · 3 citations)* Answering questions about hour-long videos is difficult because the relevant evidence is scattered and sparse. ## All works 9 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them. 1. **[Mitigating the Alignment Tax of RLHF](https://md.research.elamur.ai/free/en/papers/lin-2024-mitigating-alignment-tax-rlhf/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yong Lin, Hangyu Lin, Wei Xiong, et al.*2024 · 83 citations* 2. **[DetGPT: Detect What You Need via Reasoning ↗](https://aclanthology.org/2023.emnlp-main.876/) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yet**Renjie Pi et al.*2023 · 83 citations* 3. **[G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model ↗](https://arxiv.org/abs/2312.11370) arXiv:2312.11370 · no summary yet**Jiahui Gao et al.*2023 · 76 citations* 4. **[MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance](https://md.research.elamur.ai/free/en/papers/pi-2024-mllm-protector-ensuring-mllm-s/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Renjie Pi, Tianyang Han, Jianshu Zhang, et al.*2024 · 58 citations* 5. **[TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts ↗](https://arxiv.org/abs/2407.03203) arXiv:2407.03203 · no summary yet**Ruida Wang et al.*2024 · 51 citations* 6. [**MA-LoT: Model-Collaboration Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem Proving arXiv:2503.03205**Ruida Wang, Rui Pan, Yuxin Li, et al.*2025 · 18 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-ma-lot-model-collaboration-lean/) 7. [**Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation arXiv:2603.19220**Zhuolin Yang, Zihan Liu, Yang Chen, et al.*2026 · 16 citations*](https://md.research.elamur.ai/free/en/papers/yang-2026-nemotron-cascade-2-post-training/) 8. **[VLM2-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues](https://md.research.elamur.ai/free/en/papers/zhang-2025-vlm2-bench-closer-look-how/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianshu Zhang, Dongyu Yao, Renjie Pi, et al.*2025 · 12 citations* 9. **[LongVideoAgent: Multi-Agent Reasoning with Long Videos](https://md.research.elamur.ai/free/en/papers/liu-2026-longvideoagent-multi-agent-reasoning-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Runtao Liu, Ziyi Liu, Jiaqi Tang, et al.*2026 · 3 citations* ## Co-authors here 12 of 31 co-authors with a page, the most papers together first. - [Jipeng Zhang](https://md.research.elamur.ai/free/en/authors/jipeng-zhang/) ×4 - [Rui Pan](https://md.research.elamur.ai/free/en/authors/rui-pan/) ×3 - [Hanze Dong](https://md.research.elamur.ai/free/en/authors/hanze-dong/) ×2 - [Jianshu Zhang](https://md.research.elamur.ai/free/en/authors/jianshu-zhang/) ×2 - [Shizhe Diao](https://md.research.elamur.ai/free/en/authors/shizhe-diao/) ×2 - [Tong Zhang](https://md.research.elamur.ai/free/en/authors/tong-zhang/) ×2 - [Boxin Wang](https://md.research.elamur.ai/free/en/authors/boxin-wang/) - [Chankyu Lee](https://md.research.elamur.ai/free/en/authors/chankyu-lee/) - [Dongfu Jiang](https://md.research.elamur.ai/free/en/authors/dongfu-jiang/) - [Hangyu Lin](https://md.research.elamur.ai/free/en/authors/hangyu-lin/) - [Hanning Zhang](https://md.research.elamur.ai/free/en/authors/hanning-zhang/) - [Haoxiang Wang](https://md.research.elamur.ai/free/en/authors/haoxiang-wang/) ## 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)](https://md.research.elamur.ai/free/en/venues/emnlp/) ×3: [EMNLP 2024](https://md.research.elamur.ai/free/en/venues/emnlp/2024/) ×2 - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×2: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/), [ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/) ## Cited by Papers of our library cite their works 258 times (of the 149,439 papers whose bibliographies we read); [57 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/renjie-pi/cited-by/). Updated 31 October 2026