Ximing Lu

Publications

Publications are listed in reverse chronological order. For a list of all publications, please check out my Google Scholar

• Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction
  Yong Lin, Shange Tang, Bohan Lyu, Ziran Yang, Jui-Hui Chung, Haoyu Zhao, Lai Jiang, Yihan Geng, Jiawei Ge, Jingruo Sun, Jiayun Wu, Jiri Gesi, Ximing Lu, David Acuna, Kaiyu Yang, Hongzhou Lin, Yejin Choi, Danqi Chen, Sanjeev Arora, Chi Jin
  ICLR 2026
  
  
• DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Tree-based Search
  *Fang Wu, *Weihao Xuan, *Heli Qi, Aaron Tu, Ximing Lu, Li Erran Li, Yejin Choi
  ICLR 2026
  
  
• ProfBench: Multi-Domain Rubrics requiring Professional Knowledge to Answer and Judge
  Zhilin Wang, Jaehun Jung, Ximing Lu, Shizhe Diao, Ellie Evans, Jiaqi Zeng, Pavlo Molchanov, Yejin Choi, Jan Kautz, Yi Dong
  ICLR 2026
  
  
• Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text
  Ximing Lu, David Acuna, Jaehun Jung, Jian Hu, Di Zhang, Shizhe Diao, Yunheng Zou, Shaokun Zhang, Brandon Cui, Mingjie Liu, Hyunwoo Kim, Prithviraj Ammanabrolu, Jan Kautz, Yi Dong, Yejin Choi
  ICML 2026
  
  
• Long Grounded Thoughts: Synthesizing Visual Problems and Reasoning Chains at Scale
  *David Acuna, *Chao-Han Huck Yang, *Yuntian Deng, *Jaehun Jung, *Ximing Lu, Prithviraj Ammanabrolu, Hyunwoo Kim, Yuan-Hong Liao, Yejin Choi
  ICML 2026
  
  
• BroRL: Scaling Reinforcement Learning via Broadened Exploration
  Jian Hu, Mingjie Liu, Ximing Lu, Fang Wu, Zaid Harchaoui, Shizhe Diao, Yejin Choi, Pavlo Molchanov, Jun Yang, Jan Kautz, Yi Dong
  ICML 2026
  
  
• ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration
  *Hongjin Su, *Shizhe Diao, Ximing Lu, Mingjie Liu, Jiacheng Xu, Xin Dong, Yonggan Fu, Peter Belcak, Hanrong Ye, Hongxu Yin, Yi Dong, Evelina Bakhturina, Tao Yu, Yejin Choi, Jan Kautz, Pavlo Molchanov
  ICML 2026
  
  
• GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization
  Shih-Yang Liu, Xin Dong, Ximing Lu, Shizhe Diao, Peter Belcak, Mingjie Liu, Min-Hung Chen, Hongxu Yin, Yu-Chiang Frank Wang, Kwang-Ting Cheng, Yejin Choi, Jan Kautz, Pavlo Molchanov
  ICML 2026
  
  
• CreativityPrism: A Holistic Evaluation Framework for Large Language Model Creativity
  Zhaoyi Joey Hou, Bowei Alvin Zhang, Yining Lu, Bhiman Kumar Baghel, Anneliese Brei, Ximing Lu, Meng Jiang, Faeze Brahman, Snigdha Chaturvedi, Haw-Shiuan Chang, Daniel Khashabi, Xiang Lorraine Li
  TMLR 2026
  
  
• Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
  NVIDIA (incl. Ximing Lu)
  NVIDIA Technical Report, 2026
  
  
• ProRL Agent: Rollout-as-a-Service for RL Training of Multi-Turn LLM Agents
  Hao Zhang, Mingjie Liu, Shaokun Zhang, Songyang Han, Jian Hu, Zhenghui Jin, Yuchi Zhang, Shizhe Diao, Ximing Lu, Binfeng Xu, Zhiding Yu, Jan Kautz, Yi Dong
  NVIDIA Technical Report, 2026
  
  
• ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models
  Mingjie Liu, Shizhe Diao, Ximing Lu, Jian Hu, Xin Dong, Yejin Choi, Jan Kautz, Yi Dong
  NeurIPS 2025
  Featured: 177K views on Twitter/X · Endorsed by Nathan Lambert (AI2)
  
  
• Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning
  Jaehun Jung, *Seungju Han, *Ximing Lu, *Skyler Hallinan, David Acuna, Shrimai Prabhumoye, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Yejin Choi
  NeurIPS 2025, Spotlight
  
  
• AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text
  Ximing Lu, Melanie Sclar, Skyler Hallinan, Niloofar Mireshghallah, Jiacheng Liu, Seungju Han, Allyson Ettinger, Liwei Jiang, Khyathi Chandu, Nouha Dziri, Yejin Choi
  ICLR 2025, Oral (1.8%)
  Featured: Science Magazine (News from Science) · CGTN America · AIwire · Ploutos AI
  
  
• CertainlyUncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness
  *Khyathi Chandu, *Linjie Li, Anas Awadalla, Ximing Lu, Jae Sung Park, Jack Hessel, Lijuan Wang, Yejin Choi
  ICLR 2025
  
  
• HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions
  Xuhui Zhou, *Hyunwoo Kim, *Faeze Brahman, Liwei Jiang, Hao Zhu, Ximing Lu, Frank Xu, Bill Yuchen Lin, Yejin Choi, Niloofar Mireshghallah, Ronan Le Bras, Maarten Sap
  COLM 2025
  
  
• The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage
  Skyler Hallinan, Jaehun Jung, Melanie Sclar, Ximing Lu, Abhilasha Ravichander, Sahana Ramnath, Yejin Choi, Sai Praneeth Karimireddy, Niloofar Mireshghallah, Xiang Ren
  COLM 2025
  
  
• Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers
  *Wooseok Seo, *Seungju Han, Jaehun Jung, Benjamin Newman, Seungwon Lim, Seungbeen Lee, Ximing Lu, Yejin Choi, Youngjae Yu
  COLM 2025
  
  
• Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions
  David Acuna, Ximing Lu, Jaehun Jung, Hyunwoo Kim, Amlan Kar, Sanja Fidler, Yejin Choi
  EMNLP 2025
  
  
• Synthetic Visual Genome
  Jae Sung Park, Zixian Ma, Linjie Li, Chenhao Zheng, Cheng-Yu Hsieh, Ximing Lu, Khyathi Chandu, Quan Kong, Norimasa Kobori, Ali Farhadi, Yejin Choi, Ranjay Krishna
  CVPR 2025
  
  
• Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models
  Abhilasha Ravichander, Jillian Fisher, Taylor Sorensen, Ximing Lu, Yuchen Lin, Maria Antoniak, Niloofar Mireshghallah, Chandra Bhagavatula, Yejin Choi
  NAACL 2025
  
  
• Multi-Attribute Constraint Satisfaction via Language Model Rewriting
  Ashutosh Baheti, Debanjana Chakraborty, Faeze Brahman, Ronan Le Bras, Ximing Lu, Nouha Dziri, Yejin Choi, Mark Riedl, Maarten Sap
  TMLR 2025
  
  
• DLER: Doing Length Penalty Right - Incentivizing More Intelligence per Token via Reinforcement Learning
  Shih-Yang Liu, Xin Dong, Ximing Lu, Shizhe Diao, Mingjie Liu, Min-Hung Chen, Hongxu Yin, Yu-Chiang Frank Wang, Kwang-Ting Cheng, Yejin Choi, Jan Kautz, Pavlo Molchanov
  NVIDIA Technical Report, 2025
  
  
• NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
  NVIDIA (incl. Ximing Lu)
  NVIDIA Technical Report, 2025
  
  
• Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training
  Mingjie Liu, Shizhe Diao, Jian Hu, Ximing Lu, Xin Dong, Hao Zhang, Alexander Bukharin, Shaokun Zhang, Jiaqi Zeng, Makesh Narsimhan Sreedhar, Gerald Shen, David Mosallanezhad, Di Zhang, Jonas Yang, June Yang, Oleksii Kuchaiev, Guilin Liu, Zhiding Yu, Pavlo Molchanov, Yejin Choi, Jan Kautz, Yi Dong
  NVIDIA Technical Report, 2025
  
  
• WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models
  Liwei Jiang, *Kavel Rao, *Seungju Han, Allyson Ettinger, Faeze Brahman, Sachin Kumar, Niloofar Mireshghallah, Ximing Lu, Maarten Sap, Yejin Choi, Nouha Dziri
  NeurIPS 2024
  
  
• A Roadmap to Pluralistic Alignment
  Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, Yejin Choi
  ICML 2024
  
  
• Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement
  Linlu Qiu, Liwei Jiang, Ximing Lu, Melanie Sclar, Valentina Pyatkin, Chandra Bhagavatula, Bailin Wang, Yoon Kim, Yejin Choi, Nouha Dziri, Xiang Ren
  ICLR 2024, Oral (1.2%)
  
  
• The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
  Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu, Nouha Dziri, Melanie Sclar, Khyathi Chandu, Chandra Bhagavatula, Yejin Choi
  ICLR 2024
  
  
• The Generative AI Paradox: “What It Can Create, It May Not Understand”
  *Peter West, *Ximing Lu, *Nouha Dziri, *Faeze Brahman, *Linjie Li, Jena D. Hwang, Liwei Jiang, Jillian Fisher, Abhilasha Ravichander, Khyathi Chandu, Benjamin Newman, Pang Wei Koh, Allyson Ettinger, Yejin Choi
  ICLR 2024
  
  
• Leftover Lunch: Advantage-Based Offline Reinforcement Learning for Language Models
  Ashutosh Baheti, Ximing Lu, Faeze Brahman, Ronan Le Bras, Maarten Sap, Mark Riedl
  ICLR 2024
  
  
• Tailoring Self-Rationalizers with Multi-Reward Distillation
  Sahana Ramnath, Brihi Joshi, Skyler Hallinan, Ximing Lu, Liunian Harold Li, Aaron Chan, Jack Hessel, Yejin Choi, Xiang Ren
  ICLR 2024
  
  
• Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties
  Taylor Sorensen, Liwei Jiang, Jena D Hwang, Sydney Levine, Valentina Pyatkin, Peter West, Nouha Dziri, Ximing Lu, Kavel Rao, Chandra Bhagavatula, Maarten Sap, John Tasioulas, Yejin Choi
  AAAI 2024, Oral (top 3%)
  
  
• Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Models
  Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian Fisher, Taylor Sorensen, Yejin Choi
  NAACL 2024
  
  
• JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models
  Jillian Fisher, Ximing Lu, Jaehun Jung, Liwei Jiang, Zaid Harchaoui, Yejin Choi
  NAACL 2024
  
  
• Information-Theoretic Distillation for Reference-less Summarization
  Jaehun Jung, Ximing Lu, Liwei Jiang, Faeze Brahman, Peter West, Pang Wei Koh, Yejin Choi
  COLM 2024
  
  
• StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements
  *Jillian Fisher, *Skyler Hallinan, Ximing Lu, Mitchell Gordon, Zaid Harchaoui, Yejin Choi
  EMNLP 2024
  
  
• In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search
  Huihan Li, Yuting Ning, Zeyi Liao, Siyuan Wang, Xiang Lorraine Li, Ximing Lu, Wenting Zhao, Faeze Brahman, Yejin Choi, Xiang Ren
  EMNLP 2024
  
  
• How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models
  Jaeyoung Lee, Ximing Lu, Jack Hessel, Faeze Brahman, Youngjae Yu, Yonatan Bisk, Yejin Choi, Saadia Gabriel
  Findings of EMNLP 2024
  
  
• Faith and Fate: Limits of Transformers on Compositionality
  *Nouha Dziri, *Ximing Lu, *Melanie Sclar, +Xiang Lorraine Li, +Liwei Jiang, +Bill Yuchen Lin, Sean Welleck, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena Hwang, Soumya Sanyal, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, Yejin Choi
  NeurIPS 2023, Spotlight
  Featured: 484K views on Twitter/X · Reposted by Yann LeCun · Reposted by Kevin Murphy · Endorsed by Vitaly Kurin (NVIDIA) · YouTube (Automata Learning Lab) · YouTube (mardin mardin)
  
  
• Localized Symbolic Knowledge Distillation for Visual Commonsense Models
  Jae Sung Park, Jack Hessel, Khyathi Chandu, Paul Pu Liang, Ximing Lu, Peter West, Youngjae Yu, Qiuyuan Huang, Jianfeng Gao, Ali Farhadi, Yejin Choi
  NeurIPS 2023
  
  
• Generating Sequences by Learning to Self-Correct
  *Sean Welleck, *Ximing Lu, +Peter West, +Faeze Brahman, Tianxiao Shen, Daniel Khashabi, Yejin Choi
  ICLR 2023
  
  
• SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization
  Hyunwoo Kim, Jack Hessel, Liwei Jiang, Peter West, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Le Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap, Yejin Choi
  EMNLP 2023, Outstanding Paper Award
  
  
• Inference-time Policy Adapters (IPA): Tailoring Extreme-Scale LMs Without Fine-Tuning
  Ximing Lu, Faeze Brahman, Peter West, Jaehun Jung, Khyathi Chandu, Abhilasha Ravichander, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian Fisher, Bill Lin, Skyler Hallinan, Lianhui Qin, Xiang Ren, Sean Welleck, Yejin Choi
  EMNLP 2023
  
  
• NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation
  Peter West, Ronan Le Bras, Taylor Sorensen, Bill Yuchen Lin, Liwei Jiang, Ximing Lu, Khyathi Chandu, Jack Hessel, Ashutosh Baheti, Chandra Bhagavatula, Yejin Choi
  Findings of EMNLP 2023
  
  
• STEER: Unified Style Transfer with Expert Reinforcement
  Skyler Hallinan, Faeze Brahman, Ximing Lu, Jaehun Jung, Sean Welleck, Yejin Choi
  Findings of EMNLP 2023
  
  
• ClarifyDelphi: Reinforced Clarification Questions with Defeasibility Rewards for Social and Moral Situations
  Valentina Pyatkin, Jena D. Hwang, Vivek Srikumar, Ximing Lu, Liwei Jiang, Yejin Choi, Chandra Bhagavatula
  ACL 2023
  
  
• I2D2: Inductive Knowledge Distillation with Neurologic and Self-Imitation
  Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi
  ACL 2023
  
  
• Fusing Pre-trained Language Models with Multimodal Prompts through Reinforcement Learning
  *Youngjae Yu, *Jiwan Chung, Heeseung Yun, Jack Hessel, Jae Sung Park, Ximing Lu, Rowan Zellers, Prithviraj Ammanabrolu, Ronan Le Bras, Gunhee Kim, Yejin Choi
  CVPR 2023
  
  
• NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
  Ximing Lu, +Sean Welleck, +Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah Smith, Yejin Choi
  NAACL 2022, Best Paper Award
  
  
• Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
  Peter West, Chandra Bhagavatula, Jack Hessel, Jena D. Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi
  NAACL 2022, Oral
  
  
• Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer
  Yanpeng Zhao, Jack Hessel, Youngjae Yu, Ximing Lu, Rowan Zellers, Yejin Choi
  NAACL 2022, Oral
  
  
• Quark: Controllable Text Generation with Reinforced Unlearning
  Ximing Lu, Sean Welleck, Liwei Jiang, Jack Hessel, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, Yejin Choi
  NeurIPS 2022, Oral
  
  
• Naturalprover: Grounded Mathematical Proof Generation with Language Models
  *Sean Welleck, *Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi
  NeurIPS 2022
  
  
• MERLOT Reserve: Multimodal Neural Script Knowledge through Vision and Language and Sound
  Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, Yejin Choi
  CVPR 2022, Oral
  
  
• Generated Knowledge Prompting for Commonsense Reasoning
  Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi
  ACL 2022
  
  
• Prosocialdialog: A Prosocial Backbone for Conversational Agents
  Hyunwoo Kim, Youngjae Yu, Liwei Jiang, Ximing Lu, Daniel Khashabi, Gunhee Kim, Yejin Choi, Maarten Sap
  EMNLP 2022
  
  
• Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering
  Jiacheng Liu, Skyler Hallinan, Ximing Lu, Pengfei He, Sean Welleck, Hannaneh Hajishirzi, Yejin Choi
  EMNLP 2022
  
  
• Twist Decoding: Diverse Generators Guide Each Other
  Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. Smith
  EMNLP 2022
  
  
• End-to-End Diagnosis of Breast Biopsy Images with Transformers
  *Sachin Mehta, *Ximing Lu, Wenjun Wu, Donald Weaver, Hannaneh Hajishirzi, Joann Elmore, Linda Shapiro
  Medical Image Analysis, 2022
  
  
• 🍷MERLOT: Multimodal Neural Script Knowledge Models
  *Rowan Zellers, *Ximing Lu, *Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, Yejin Choi
  NeurIPS 2021, Oral (1%)
  
  
• DExperts: On-the-Fly Controlled Text Generation with Experts and Anti-Experts
  Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah Smith, Yejin Choi
  ACL 2021, Oral
  
  
• Reflective Decoding: Beyond Unidirectional Generation with Off-the-shelf Language Models
  Peter West, Ximing Lu, Ari Holtzman, Chandra Bhagavatula, Jena D. Hwang, Yejin Choi
  ACL 2021
  
  
• On-the-Fly Attention Modulation for Neural Generation
  Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung, Yejin Choi
  ACL 2021 Findings
  
  
• NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints
  Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi
  NAACL 2021
  
  
• Analyzing Commonsense Emergence in Few-shot Knowledge Models
  Jeff Da, Ronan Le Bras, Ximing Lu, Yejin Choi, Antoine Bosselut
  AKBC 2021
  
  
• Analysis of Regions of Interest and Distractor Regions in Breast Biopsy Images
  Ximing Lu, Sachin Mehta, Tad Brunyé, Donald Weaver, Joann Elmore, Linda Shapiro
  BHI 2021
  
  
• Applications of the ESPNet Architecture in Medical Imaging
  Sachin Mehta, Nicholas Nuechterlein, Ezgi Mercan, Beibin Li, Shima Nofallah, Wenjun Wu, Ximing Lu, Anat Caspi, Mohammad Rastegari, Joann Elmore, Hannaneh Hajishirzi, Linda Shapiro
  Book chapter, State of the Art in Neural Networks and their Applications, 117-131
  

Preprints (Under Review)

• Introspective X Training: Feedback Conditioning Improves Scaling Across all LLM Training Stages
  Brandon Cui, Ximing Lu, Jaehun Jung, Syeda Nahida Akter, Hyunwoo Kim, Yuxiao Qu, David Acuna, Shrimai Prabhumoye, Yejin Choi, Prithviraj Ammanabrolu
  arXiv:2605.20285
  
  
• DeltaPrompts: Escaping the Zero-Delta Trap in Multimodal Distillation
  Jaehun Jung, Hyunwoo Kim, Brandon Cui, Ximing Lu, David Acuna, Prithviraj Ammanabrolu, Yejin Choi
  arXiv:2605.15532
  
  
• iGRPO: Self-Feedback-Driven LLM Reasoning
  Ali Hatamizadeh, Shrimai Prabhumoye, Igor Gitman, Ximing Lu, Seungju Han, Wei Ping, Yejin Choi, Jan Kautz
  arXiv:2602.09000
  
  
• The Invisible Leash: Why RLVR May or May Not Escape Its Origin
  Fang Wu, Weihao Xuan, Ximing Lu, Mingjie Liu, Yi Dong, Zaid Harchaoui, Yejin Choi
  arXiv:2507.14843
  
  
• Retro-Search: Exploring Untaken Paths for Deeper and Efficient Reasoning
  Ximing Lu, Seungju Han, David Acuna, Hyunwoo Kim, Jaehun Jung, Shrimai Prabhumoye, Niklas Muennighoff, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Yejin Choi
  arXiv:2504.04383
  

Media Coverage

AI as Humanity's Salieri (ICLR 2025)

• Science (AAAS), News from Science — "AI writing is improving, but it still can't match human creativity," by Matthew Hutson
• CGTN America — World Today segment "Can AI Match Human Creativity?", by Mark Niu
• AIwire — "Despite Rapid Advances, Studies Show AI Can't Match Human Creativity," by Ali Azhar
• Ploutos AI — Featured research stream

Faith and Fate (NeurIPS 2023)

• Twitter/X — post about the paper reached 484,000 views
• Yann LeCun (Turing Award; former Chief AI Scientist, Meta) reposted the paper: "The evidence is accumulating."
• Kevin Murphy (Research Scientist, Google DeepMind) reposted with his analysis of compounding errors in long reasoning chains
• Vitaly Kurin (Senior Research Scientist, NVIDIA): "Best paper I've read in a while. No excessive hype, clear question, rigorous empirical evaluation. Highly recommended."
• YouTube — in-depth analyses by Automata Learning Lab and mardin mardin

ProRL (NeurIPS 2025)

• Twitter/X — post about the paper reached 177,000 views
• Nathan Lambert (reinforcement learning researcher, Allen Institute for AI; author of The RLHF Book) endorsed ProRL, highlighting its RL scaling results on a 1.5B model

Honors & Awards

• (2023) Outstanding Paper Award at EMNLP
  
• (2023) Best Senior Thesis Award, Paul G. Allen School of Computer Science & Engineering
  
• (2022) Best Paper Award at NAACL
  
• (2020) Outstanding Undergraduate Researcher Award Runners-Up, Computing Research Association
  
• (2020) Lisa Simonyi Prize, Paul G. Allen School of Computer Science & Engineering
  
• (2020) Levinson Emerging Scholars Award, University of Washington
  
• (2020) Mary Gates Research Scholarship, University of Washington
  
• (2019) Denton, Denice Dee Scholars Endowment, Paul G. Allen School of Computer Science & Engineering
  
• (2018) Second Prize of UW Datathon, Citadel Investment Group, LLC
  
• (2018) Conference Travel Award, University of Washington
  

Teaching Experience

• (Winter, 2024) TA @ CSE 447/517 (Undergrad/Grad NLP) at University of Washington
• (Winter, 2021)-TA @ CSE P517 (Professional NLP) at University of Washington