Preference Learning

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9 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Latest papers 56

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Logic-Logit: A Logic-Based Approach to Choice Modeling
Oct 4, 2026Shuhan Zhang, Wendi Ren, Shuang LiDiscrete Choice Models Preference Learning
CUHK-Shenzhen
Inherited Learning in an Artificial Ecology: How Controls and Update Allocation Shape Benefits
Oct 1, 2026Xuening Wu, Lei Li, Shan YuPreference Learning Learn
Pfizer, Shanghai, China · Independent Researcher
On the Complexity of Preference-Based Bandits
Sep 30, 2026Ahmed Ben Yahmed, Marc Abeille, Clément CalauzènesPreference Learning
CREST, ENSAE Paris, FAIRPLAY · FAIRPLAY
Optimal Design for Active Preference Learning with Biased LLM Judges
Sep 30, 2026Zhongman Du, Huiming Zhang, Haodong Zhu +1Preference Learning Preference Alignment Learning
Beihang University · Zhongguancun Academy · Hangzhou Innovation Institute of Beihang University
Context-Continuous Preference Learning for Exoskeleton Personalization
Sep 23, 2026Sunin Baek, Sungwoo Park, Daekyum KimPreference Learning Exoskeleton
School of Mechanical Engineering, Korea University, Seoul 02841, South Korea
Which Terrain Is Better? Preference Learning with VLM Prototypes for Off-Road Traversability Ranking
Sep 20, 2026Ji-Hoon Hwang, Jisung Bae, E-In Son +3Traversability Terrain
Learning Heterogeneous Preferences
Sep 15, 2026Shiwali Mohan, Matt Hong, Dule Shu +3Preference Learning Preference Alignment Learning
Human-Centered AI Toyota Research Institute Los Altos, CA
Preference Optimization with LALM Feedback for Continuous Autoregressive Non-Verbal Vocalization Generation
Sep 10, 2026Jingbin Hu, Qirui Zhan, Yuang Cao +7Preference Learning Feedback
Shenzhen Pimei Technology Co., Ltd., Guangdong, China · Yutu Zhineng, Beijing, China · State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China +1
You Can't Prefer Emotions You Don't Sample: Intensity Undershoot in DPO-Tuned LLMs
Sep 7, 2026Hyunwoo Kim, Usama KhalidValence-Arousal Estimation Emotion
Department of Computer Software Hanyang University, Seoul
PLC-DPO: Posterior Label Correction in Noisy and Ambiguous Preference Optimization
Aug 31, 2026Boryeong Cho, Sumyeong Ahn, Se-Young YunBacktranslation Augmented Direct Preference Optimization Preference Learning
KAIST AI · KENTECH
Interoceptive Attention as Dynamic Homeostatic Prioritization in a Foraging Agent
Aug 4, 2026St John Grimbly, Nicolas Kuske, Evert A. Boonstra +7Predator-Prey Models Preference Learning
Dept. of Mathematics & Applied Mathematics, Univ. of Cape Town, South Africa · Neuroscience Institute, Univ. of Cape Town, South Africa · MIND Institute and CSAM, University of the Witwatersrand, South Africa +3
An analysis of machine learning approaches for enhancing decision-making in complex discrete choice tasks
Jul 30, 2026Sheng Lun Christine Cao, Destenie Nock, Alex DavisDiscrete Choice Models Preference Learning
Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada · Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA · Department of Civil Engineering, Carnegie Mellon University, Pittsburgh, PA, USA +1
Synchronizing Beliefs with Second-Order Theory-of-Mind in Human-Autonomy Teams (Extended Version)
Jul 29, 2026Jack Mirenzi, Henny AdmoniPreference Learning Reinforcement Learning From Human Feedback
Robotics Institute, Carnegie Mellon University
TELLER: Dual-Path Iterative Preference Optimization for Table Entity Linking
Jul 29, 2026Yixin Peng, Kehao Li, Stefan DeckerEntities Backtranslation Augmented Direct Preference Optimization
RWTH Aachen, Ahornstraße 55, 52074 Aachen, Germany
Learning Dynamic User Personas from Implicit Interaction Streams via Iterative Refinement
Jul 29, 2026Haifeng WuArtificial Intelligence Personas Preference Learning
PayPal
Step-Level Preference Learning for Generative Agents in Social Simulations
Jul 16, 2026Wenchang Gao, Pingyue Sheng, Lanlan Qiu +7Preference Learning Simulation-Based Reinforcement Learning
Shanghai Qi Zhi Institute · Tsinghua University · Xiongan AI Institute +3
Internal Pluralism and the Limits of Pairwise Comparisons
Jul 2, 2026Bailey Flanigan, Michelle SiInternal Pluralism Preference Learning
MIT, USA · Harvard, USA
SPLC: Social Preference Learning for Crowd Robot Navigation
Jul 2, 2026Zixuan Chen, Hao Fu, Haiwen Hu +1Robot Navigation Human-Robot Collaboration
Wuhan University of Science and Technology, Wuhan, China · School of Computer Science and Technology, Wuhan University of Science and Technology and also with the Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430081, China
Scaling Trends for Lie Detector Oversight in Preference Learning
Jul 2, 2026Oskar J. Hollinsworth, Ann-Kathrin Dombrowski, Sam Adam-Day +2Deception Preference Learning
Freeform Preference Learning for Robotic Manipulation
Jun 30, 2026Marcel Torne, Anubha Mahajan, Abhijnya Bhat +1Robot Policies Robotic Manipulation
Stanford University
MAPL: Multi-Objective Preference Learning for Robot Locomotion
Jun 24, 2026Xiyue Chen, Muhan Lin, Shuyang Shi +1Multi-Objective Reinforcement Learning Preference Learning
Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47906, USA · Department of Computer Science, Purdue University, West Lafayette, IN 47906, USA · independent researcher
Towards Spec Learning: Inference-Time Alignment from Preference Pairs
Jun 22, 2026Dhriti Krishnan, Tejas Goyal, Jaromir SavelkaPreference Learning Preference Alignment Learning
Department of Computer Science Carnegie Mellon University
Self-Evolution for Multi-Turn Tool-Calling Agents via Divergence-Point Preference Learning
Jun 22, 2026Jiaqiang TangPreference Learning Self-Evolution
The Hong Kong University of Science and Technology (Guangzhou)
Improving Code-Switching ASR with Code-Mixing Guided Synthetic Speech
Jun 14, 2026Yue Heng Yeo, Haoyang Li, Yizhou Peng +6Code-Switching Automatic Speech Recognition
College of Computing and Data Science, Nanyang Technological University, Singapore · HLT-COE & CLSP, Johns Hopkins University, USA · Institute for Infocomm Research (I2R), A⋆STAR, Singapore +1
S $^2$ COPE: Self-Supervised Concept Discovery via Preference Learning
Jun 12, 2026Shilong Xiang, Zirui Zhang, Chengzhi MaoUnsupervised Preference Learning
Rutgers University
PAWS: Preference Learning with Advantage-Weighted Segments
Jun 10, 2026Aleksandar Taranovic, Onur Celik, Niklas Freymuth +6Preference Learning Trajectory-Level Credit
Autonomous Learning Robots, Karlsruhe Institute of Technology, Karlsruhe, Germany · Bosch Center for Artificial Intelligence, Renningen, Germany · Institute for Material Handling and Logistics (IFL), Karlsruhe Institute of Technology, Karlsruhe, Germany
A Regret Minimization Framework on Preference Learning in Large Language Models
Jun 8, 2026Suhwan Kim, Taehyun Cho, Geon-Hyeong Kim +4Reinforcement Learning From Human Feedback Sequential Preference Optimization
Seoul National University, Seoul, South Korea · LG AI Research, Seoul, South Korea · UNIST, Ulsan, South Korea +1
Statistical Priors for Implicit Preferences: Decoupling Skill Selection as a Local Harness in Personal Agents
Jun 4, 2026Zeyu Gan, Huayi Tang, Yong LiuPreference Learning Large Language Model Personalization
Gaoling School of Artificial Intelligence Renmin University of China Beijing, China
ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning
Jun 2, 2026Ziyan Liu, Xueda Shen, Yuzhe Gu +7Large Reasoning Models Reasoning Chain
Shanghai Artificial Intelligence Laboratory · University of Science and Technology of China · MMLab, The Chinese University of Hong Kong
Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning
May 30, 2026Chishui Chen, Jiaye Lin, Te Sun +6Skills Agentic Reinforcement Learning
Meituan · Shanghai Jiao Tong University · Nanjing University +2
Isolating LLM Lexical Bias: A Curation-Free Triangulated Metric for Preference-Stage Learning
May 29, 2026Xiaoyang Ming, Jose Hernandez, Thomas Stephan JuzekLarge Language Model Bias Large Language Model Alignment
Florida State University
Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences
May 29, 2026Jabin Koo, Hoyoung Kim, Minwoo Jang +1Preference Learning Preference Alignment Learning
Department of CSE, POSTECH, Pohang, Republic of Korea · National AI Research Lab, Seoul, Republic of Korea · Graduate School of AI, POSTECH, Pohang, Republic of Korea
Reward Learning from Best-of- $N$ Preference Data: Targets, Tradeoffs, and Design Principles
May 28, 2026Rattana Pukdee, Maria-Florina Balcan, Pradeep RavikumarBest-Of-N Preference Learning
Machine Learning Department Carnegie Mellon University
Calibrated Preference Learning: The Case of Label Ranking
May 28, 2026Santo M. A. R. Thies, Viktor Bengs, Timo Kaufmann +2Ranking Preference Learning
Data Science and its Applications, Deutsches Forschungsinstitut für Künstliche Intelligenz (DFKI), Kaiserslautern, Germany · Munich Center for Machine Learning, Munich (MCML), Germany · Ludwig Maximilian Universität München, Munich, Germany
Hallucination Detection-Guided Preference Optimization for Clinical Summarization
May 27, 2026Shamanth Kuthpadi Seethakantha, Dung Ngoc Thai, Vara Prasad Gudi +6Summarization Hallucination Detection
1Manning College of Information and Computer Sciences · 2Ensemble HP · 3Columbia University +1
Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics
May 26, 2026Yuxuan Ye, Raul Santos-Rodriguez, Edwin SimpsonSummarization Facts
Intelligent System Laboratory University of Bristol United Kingdom
Active Query Synthesis for Preference Learning
May 25, 2026Namrata Nadagouda, Nauman Ahad, Maegan Tucker +1Preference Learning Pool-Based Active Learning
Georgia Institute of Technology · Gauss Labs
When In-Distribution Gains Fail: Evaluating Weak-to-Strong Reward Models under Preference Shift
May 25, 2026Khoi Le, Tri Cao, Phong Nguyen +5Preference Learning Preference Alignment Learning
National University of Singapore · VinUniversity · Nanyang Technological University
Learning Kernel-Based MDPs from Episodic Preferential Feedback
May 22, 2026Nikola Pavlovic, Sattar Vakili, Qing ZhaoReinforcement Learning From Human Feedback Preference Learning
Cornell University · MediaTek Research
Convex Optimization for Alignment and Preference Learning on a Single GPU
May 22, 2026Miria Feng, Mert PilanciPreference Learning Convex Optimization
Department of Electrical Engineering, Stanford University, California, United States.
Progressive Autonomy as Preference Learning: A Formalization of Trust Calibration for Agentic Tool Use
May 18, 2026Changkun OuAI Trustworthiness Preference Learning
SyncDPO: Enhancing Temporal Synchronization in Video-Audio Joint Generation via Preference Learning
May 12, 2026Xin Cheng, Xihua Wang, Ying Ba +5Audio-Video Generation Speech-To-Text Alignment
Renmin University of China · Westlake University
Learning the Preferences of a Learning Agent
May 9, 2026Karim Abdel Sadek, Mark Bedaywi, Rhys Gould +1Preference Learning Reinforcement Learning From Human Feedback
University of California, Berkeley
Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation
May 9, 2026Yang Xiao, Huiyuan Chen, Kaiyuan Deng +5Short Video Bursts Video Compression
University of Tulsa · Amazon · University of Arizona +1
Multi-Objective Constraint Inference using Inverse reinforcement learning
May 7, 2026Syed Ihtesham Hussain Shah, Floris den Hengst, Aneta Lisowska +1Multi-Objective Reinforcement Learning Constraint Satisfaction
Faculty of Sciences Vrije Universiteit Amsterdam
Preference Instability in Reward Models: Detection and Mitigation via Sparse Autoencoders
May 7, 2026Shunchang Liu, Xin Chen, Belen Martin Urcelay +1Preference Learning Preference Alignment Learning
ETH Zürich · Georgia Institute of Technology · ELLIS Institute Finland +1
PREFER: Personalized Review Summarization with Online Preference Learning
May 7, 2026Millend Roy, Agostino Capponi, Vineet GoyalSummarization Preference Learning
Department of Industrial Engineering and Operations Research Columbia University New York, NY 10027
Preference-Based Self-Distillation: Beyond KL Matching via Reward Regularization
May 6, 2026Xin Yu, Liuchen Liao, Yiwen Zhang +3Unsupervised On-Policy Self-Distillation Kullback-Leibler Divergence
1The Pennsylvania State University · 2TikTok
PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning
May 1, 2026Ziqin Yuan, Ruiqi Wang, Dezhong Zhao +2Preference Learning
1Purdue University, West Lafayette, IN, USA. · 2Beijing University of Chemical Technology, Beijing, China. · 3Indiana University Bloomington, Bloomington, IN, USA.
Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs
Apr 27, 2026Byeonggeuk Lim, JungMin Yun, Junehyoung Kwon +2Hallucination Mitigation Preference Datasets
Graduate School of Advanced Imaging Sciences, Multimedia and Film, Chung-Ang University · Department of Artificial Intelligence, Chung-Ang University
Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback
Apr 22, 2026Rongtao Zhang, Xin Zhu, Masoume Pourebadi Khotbehsara +3Preference Learning Haptic Feedback
Department of Computer Science University of Southern California Los Angeles, California, USA
PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models
Apr 21, 2026Salvatore Greco, Jacek Karolczak, Roman Słowiński +1Explainable Artificial Intelligence Preference Learning
University of Catania, Department of Economics and Business, Corso Italia 55, Catania, 95129, Italy · Poznan University of Technology, Institute of Computing Science,2026 Piotrowo 2, Poznań, 60-965, Poland · bPoznan University of Technology, Institute of Computing Science,2026 Piotrowo 2, Poznań, 60-965, Poland +1
Cat-DPO: Category-Adaptive Safety Alignment
Apr 19, 2026Tiankai Yang, Yi Nian, Xinyuan Li +3Safety Alignment Large Language Model Alignment
University of Southern California · 2Northwestern University
Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion
Apr 16, 2026Xiangrui Xiong, Hang Liang, Baiyang Chen +2Preference Learning Large Language Model Uncertainty
Accidenta
N2M: Bridging Navigation and Manipulation by Learning Pose Preference from Rollout
Sep 23, 2025Kaixin Chai, Hyunjun Lee, Joseph J. LimRobotic Manipulation Policies Preference Learning
1KAIST · 2Seoul National University · 3Vital Robotics.
Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds
Jun 27, 2025Edward Chen, Sang T. Truong, Natalie Dullerud +2Pareto Frontier Preference Learning
Department of Computer Science, Stanford University, Stanford, California, USA

Oct 4, 2026, cs.LGJ/K move · Enter open · S save

Logic-Logit: A Logic-Based Approach to Choice Modeling

Shuhan Zhang, Wendi Ren, Shuang Li

CUHK-Shenzhen

Discrete Choice Models Preference Learning

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In this study, we propose a novel rule-based interpretable choice model, Logic-Logit, designed to effectively learn and explain human choices. Choice models have been widely applied across various domains---such as commercial demand forecasting, recommendation systems, and consumer behavior analysis---typically categorized as parametric, nonparametric, or deep network-based. While recent innovations have favored neural network approaches for their computational power, these flexible models often involve large parameter sets and lack interpretability, limiting their effectiveness in contexts where transparency is essential. Previous empirical evidence shows that individuals usually use heuristic decision rules to form their consideration sets, from which they then choose. These rules are often represented as disjunctions of conjunctions (i.e., OR-of-ANDs). These rules-driven, consider-then-choose decision processes enable people to quickly screen numerous alternatives while reducing cognitive and search costs. Motivated by this insight, our approach leverages logic rules to elucidate human choices, providing a fresh perspective on preference modeling. We introduce a unique combination of column generation techniques and the Frank-Wolfe algorithm to facilitate efficient rule extraction for preference modeling---a process recognized as NP-hard. Our empirical evaluation, conducted on both synthetic datasets and real-world data from commercial and healthcare domains, demonstrates that Logic-Logit significantly outperforms baseline models in terms of interpretability and accuracy.

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