LLM Alignment

LLM: Large Language Model

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48 papers in the last four weeks, up 118% on the four weeks before. 0.5% of all new papers.

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CoAct: Co-Active LLM Preference Learning with Human-AI Synergy
Apr 19, 2026Ruiyao Xu, Mihir Parmar, Tiankai Yang +3RL for Language Models LLM Alignment
Northwestern University · Google · University of Southern California +1
Jupiter-N Technical Report
Apr 19, 2026George DraysonLLM Alignment Cultural Alignment
Locai Labs, University College London
Cat-DPO: Category-Adaptive Safety Alignment
Apr 19, 2026Tiankai Yang, Yi Nian, Xinyuan Li +3LLM Alignment Direct Preference Optimization
University of Southern California · 2Northwestern University
TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles
Apr 19, 2026Yirong Zeng, Yufei Liu, Xiao Ding +9Reward Modeling LLM Alignment
Harbin Institute of Technology SCIR Lab · Peking University · Huawei Technologies Co., Ltd
HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification
Apr 19, 2026Ziqing Wang, Kaize DingLLM Alignment Graph SSL
Northwestern University
Demystifying the unreasonable effectiveness of online alignment methods
Apr 19, 2026Enoch Hyunwook KangLLM Alignment Direct Preference Optimization
Foster School of Business University of Washington
The Granularity Gap: A Multi-Dimensional Longitudinal Audit of Sycophancy in Gemini Models
Apr 19, 2026Patrick KeoughLLM Sycophancy LLM Alignment
Independent Researcher Bremerton, WA, USA
Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding
Apr 19, 2026Lin Zhong, Siyu Zhu, Zizhen Yuan +5LLM Alignment Cognitive Modeling
Harbin Institute of Technology, Shenzhen, China · City University of Macau, Macao SAR, China · Peng Cheng Laboratory, Shenzhen, China
Alignment Imprint: Zero-Shot AI-Generated Text Detection via Provable Preference Discrepancy
Apr 18, 2026Junxi Wu, Kailin Huang, Dongjian Hu +4LLM Alignment AI-Generated Text Detection
Nankai University · Tsinghua University · Wuhan University +3
On the Rejection Criterion for Proxy-based Test-time Alignment
Apr 17, 2026Ayoub Hammal, Pierre Zweigenbaum, Caio CorroLLM Alignment Inference-Time Language Model Alignment
Université Paris-Saclay, CNRS, LISN · INSA Rennes, IRISA, CNRS, Université de Rennes
A Systematic Study of Training-Free Methods for Trustworthy Large Language Models
Apr 17, 2026Wai Man Si, Mingjie Li, Michael Backes +1LLM Alignment LLM Safety
CISPA Helmholtz Center for Information Security
GroupDPO: Memory-Efficient Group-Wise Direct Preference Optimization
Apr 17, 2026Jixuan Leng, Si Si, Hsiang-Fu Yu +2LLM Alignment Direct Preference Optimization
Carnegie Mellon University · Google DeepMind · Google
Large Language Models Generate Harmful Responses Using a Distinct Mechanism, Shared Across Harm Types
Apr 10, 2026Hadas Orgad, Boyi Wei, Kaden Zheng +4LLM Alignment Emergent Misalignment in Language Models
Kempner Institute, Harvard University · Princeton University · Harvard University +2
Learning Concepts, Not Tokens: Self-Supervised Semantic Alignment for Language Models
Mar 31, 2026Christine Zhang, Dan Jurafsky, Chen ShaniLanguage Model Pretraining LLM Alignment
Stanford University
Unbiased Reward Modeling from Implicit Feedback for LLM Alignment
Mar 24, 2026Hao Wang, Haocheng Yang, Licheng Pan +7Reward Modeling LLM Alignment
Peking University · Xiaohongshu Inc. · National University of Singapore +1
Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models
Mar 21, 2026Xinyue Liu, Niloofar Mireshghallah, Jane C. Ginsburg +1LLM Alignment Memorization in Language Models
Stony Brook University · Carnegie Mellon University · Columbia Law School
Domain Mixture Design via Log-Likelihood Differences for Aligning Language Models with a Target Model
Mar 17, 2026Ryo Kishino, Riku Shiomi, Hiroaki Yamagiwa +2Language Model Pretraining LLM Alignment
1Kyoto University · 2RIKEN
PA3: Policy-Aware Agent Alignment through Chain-of-Thought
Mar 15, 2026Shubhashis Roy Dipta, Daniel Bis, Kun Zhou +4LLM Alignment RL for Language Model Reasoning
University of Maryland, Baltimore County · Amazon Alexa AI
Design-MLLM: A Reinforcement Alignment Framework for Verifiable and Aesthetic Interior Design
Mar 4, 2026Yuxuan Yang, Xiaotong Mao, Jingyao WangLLM Alignment Indoor Scene Generation
Nanjing Forestry University, Nanjing, China · Université de Lorraine, Nancy, France · Laboratoire Réactions et Génie des Procédés, Nancy, France +2
Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
Feb 17, 2026Jialu Wang, Heinrich Peters, Asad A. Butt +6LLM Alignment Group Relative Policy Optimization
1Apple Inc., California, United States.
Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges
Feb 14, 2026Ruomeng Ding, Yifei Pang, He Sun +3LLM Alignment LLM-as-a-Judge
University of North Carolina at Chapel Hill · 2Carnegie Mellon University · 3Yale University +1
Mitigating the Safety-utility Trade-off in LLM Alignment via Adaptive Safe Context Learning
Feb 14, 2026Yanbo Wang, Minzheng Wang, Jian Liang +3LLM Alignment RL for Language Model Reasoning
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China. · NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China. · Ritzz-AI.
Evaluating Alignment of Behavioral Dispositions in LLMs
Feb 11, 2026Amir Taubenfeld, Zorik Gekhman, Lior Nezry +8Human Preference Evaluation LLM Alignment
Google Research · Hebrew University · University of Cambridge
Routing-Aware Safety Alignment for Mixture-of-Experts Models
Feb 4, 2026Jiacheng Liang, Yuhui Wang, Tanqiu Jiang +1Mixture-of-Experts Language Models LLM Alignment
Department of Computer Science Stony Brook University
Continuous-Utility Direct Preference Optimization
Jan 31, 2026Muhammad Ahmed Mohsin, Muhammad Umer, Emily FoxLLM Alignment Direct Preference Optimization
Stanford University, Stanford, California, USA
VISPA: Pluralistic Alignment via Automatic Value Selection and Activation
Jan 19, 2026Shenyan Zheng, Jiayou Zhong, Anudeex Shetty +3LLM Alignment Multi-Objective Language Model Alignment
University of Waterloo · University of Melbourne · Macquarie University +2
Untangling Input Language from Reasoning Language: A Diagnostic Framework for Cross-Lingual Moral Alignment in LLMs
Jan 15, 2026Nan Li, Bo Kang, Tijl De BieMultilingual Language Model Evaluation LLM Alignment
IDLab, Department of Electronics and Information Systems Ghent University, Belgium
Asymptotic Universal Alignment: A New Alignment Framework via Test-Time Scaling
Jan 13, 2026Yang Cai, Weiqiang ZhengLLM Alignment Test-Time Scaling
Yale University
Identifying and Mitigating Bottlenecks in Role-Playing Agents: A Systematic Study of Disentangling Character Profile Axes
Jan 8, 2026Yonghyun Jun, Junhyuk Choi, Jeonghyun Park +3LLM Alignment Personality Modeling in Language Models
Chung-Ang University, Seoul, Korea
Safety Is Not Universal: The Selective Safety Trap in LLM Alignment
Jan 7, 2026Iago Alves Brito, Walcy Santos Rezende Rios, Julia Soares Dollis +2LLM Safety Benchmarks LLM Alignment
Advanced Knowledge Center for Immersive Technologies (AKCIT) Federal University of Goiás (UFG)

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CoAct: Co-Active LLM Preference Learning with Human-AI Synergy

Ruiyao Xu, Mihir Parmar, Tiankai Yang, Zhengyu Hu+2

Northwestern University · Google · University of Southern California+1

RL for Language Models LLM Alignment Active Learning Human-in-the-Loop AI

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Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks. However, high-quality human-annotated preference data remains expensive and scarce. Existing methods address this challenge through either self-rewarding, which scales by using purely AI-generated labels but risks unreliability, or active learning, which ensures quality through oracle annotation but cannot fully leverage unlabeled data. In this paper, we present CoAct, a novel framework that synergistically combines self-rewarding and active learning through strategic human-AI collaboration. CoAct leverages self-consistency to identify both reliable self-labeled data and samples that require oracle verification. Additionally, oracle feedback guides the model to generate new instructions within its solvable capability. Evaluated on three reasoning benchmarks across two model families, CoAct achieves average improvements of +13.25% on GSM8K, +8.19% on MATH, and +13.16% on WebInstruct, consistently outperforming all baselines.

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