RL from Human Feedback

RL: Reinforcement Learning

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5 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

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Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care
Apr 30, 2026Prabhjot Singh, Abhishek Gupta, Chris Betz +4Clinical Decision Support Preference Learning
Altitude
Uncertainty-Aware Reward Discounting for Mitigating Reward Hacking
Apr 29, 2026Disha SinghaReward Hacking Reinforcement Learning
Three Models of RLHF Annotation: Extension, Evidence, and Authority
Apr 28, 2026Steve CoyneLLM Alignment Human-in-the-Loop Annotation
University of Toronto, Canada · University of Toronto, Toronto, Canada
When Errors Can Be Beneficial: A Categorization of Imperfect Rewards for Policy Gradient
Apr 28, 2026Shuning Shang, Hubert Strauss, Stanley Wei +2Reward Modeling Policy Gradient
Princeton Language and Intelligence, Princeton University
Leveraging Human Feedback for Semantically-Relevant Skill Discovery
Apr 27, 2026Maxence Hussonnois, Thommen George Karimpanal, Santu RanaAgent Skill Learning RL from Human Feedback
A2I2, Deakin University, Geelong, Australia · School of IT, Deakin University, Geelong, Australia.
HP-Edit: A Human-Preference Post-Training Framework for Image Editing
Apr 21, 2026Fan Li, Chonghuinan Wang, Lina Lei +9Diffusion Model Alignment Image Editing Evaluation
Huawei Noah’s Ark Lab · Nankai University · Harbin Institute of Technology
Policy Gradient Primal-Dual Method for Safe Reinforcement Learning from Human Feedback
Apr 21, 2026Qiang Liu, Adrienne Kline, Ermin WeiConstrained RL Policy Gradient Methods
Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA · Center for Artificial Intelligence, Bluhm Cardiovascular Institute, Northwestern Medicine, Chicago, IL, USA · Department of Surgery, Northwestern University, Chicago, IL, USA +3
ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System
Apr 20, 2026Jiacheng Liang, Yao Ma, Tharindu Kumarage +5Reward Modeling Adversarial Attacks on LLMs
Stony Brook University · Amazon Nova Responsible AI
Efficient Federated RLHF via Zeroth-Order Policy Optimization
Apr 20, 2026Deyi Wang, Qining Zhang, Lei YingFederated RL Communication-Efficient Distributed Training
University of Michigan, Ann Arbor
Demystifying the unreasonable effectiveness of online alignment methods
Apr 19, 2026Enoch Hyunwook KangLLM Alignment Direct Preference Optimization
Foster School of Business University of Washington
Rater State Bias in RLHF Preference Data: An Audit Framework
Apr 14, 2026Elena Kopteva, Vitaliy Hlynianyi-ZhukHuman Preference Evaluation Human-in-the-Loop Annotation
The Grainger College of Engineering, Department of Physics & Illinois Center for Advanced Studies of the Universe, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA · Faculty of Applied Mathematics, Oles Honchar Dnipro National University, Dnipro, 49045, Ukraine · Department of Clinical Psychology, Kyiv Institute of Modern Psychology and Psychotherapy, Kyiv, 01133, Ukraine
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
Gradient Regularization Mitigates Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards
Feb 20, 2026Johannes Ackermann, Michael Noukhovitch, Takashi Ishida +1Reward Hacking Reinforcement Learning with Verifiable Rewards
The University of Tokyo · Mila · RIKEN AIP
Joint Reward Modeling: Internalizing Chain-of-Thought for Efficient Visual Reward Models
Feb 7, 2026Yankai Yang, Yancheng Long, Hongyang Wei +12Reward Modeling Multimodal Reward Modeling
Harbin Institute of Technology, Shenzhen. · Kuaishou Technology.
Adversarial Reward Auditing for Active Detection and Mitigation of Reward Hacking
Feb 2, 2026Mohammad Beigi, Ming Jin, Junshan Zhang +2Adversarial Training Reward Hacking
Department of Computer Science, University of California, Davis, USA · Virginia Tech, Blacksburg, USA · Meta AI Research.
Adaptive Margin RLHF via Preference over Preferences
Sep 26, 2025Yaswanth Chittepu, Prasann Singhal, Greg Durrett +1Pairwise Preference Learning Reward Modeling
University of Massachusetts Amherst · University of Texas at Austin
Aligning Audio Captions with Human Preferences
Sep 18, 2025Kartik Hegde, Rehana Mahfuz, Yinyi Guo +1Reward Modeling Audio-Language Models
Qualcomm Technologies, Inc., India · Qualcomm Technologies, Inc., USA
DPO Unchained: Your Training Algorithm is Secretly Disentangled in Human Choice Theory (and its Loss' Convexity is Dispensable)
Jul 10, 2025Wenxuan Zhou, Shujian Zhang, Brice Magdalou +4Direct Preference Optimization Human Preference Modeling
Google DeepMind · CEE-M, Univ Montpellier, CNRS, INRAE, Institut Agro · Work done while at Google DeepMind +1
A Technical Survey of Reinforcement Learning Techniques for Large Language Models
Jul 5, 2025Saksham Sahai Srivastava, Vaneet AggarwalRL for Language Models LLM Alignment
University of Georgia, Athens, GA, USA · Purdue University, West Lafayette, IN, USA
MiCRo: Mixture Modeling and Context-aware Routing for Personalized Preference Learning
May 30, 2025Jingyan Shen, Jiarui Yao, Rui Yang +5Pairwise Preference Learning Reward Modeling
New York University · University of Illinois Urbana-Champaign · Rice University
Bias Fitting to Mitigate Length Bias of Reward Model in RLHF
May 19, 2025Kangwen Zhao, Jianfeng Cai, Jinhua Zhu +5Reward Modeling LLM Alignment
University of Science and Technology of China
Reinforcement Learning from Human Feedback
Apr 16, 2025Nathan LambertLLM Alignment Direct Preference Optimization
Distributionally Robust Reinforcement Learning with Human Feedback
Mar 1, 2025Debmalya Mandal, Paulius Sasnauskas, Goran RadanovicDirect Preference Optimization LLM Fine-Tuning
Dept. of Computer Science University of Warwick, UK · Department of Computing Science University of Alberta, Edmonton, Canada · Max-Planck Institute for Software Systems, Germany
Corruption Robust Offline Reinforcement Learning with Human Feedback
Feb 9, 2024Debmalya Mandal, Andi Nika, Parameswaran Kamalaruban +2Offline RL Corruption Robustness
Dept. of Computer Science University of Warwick, UK · Max-Planck Institute for Software Systems, Germany · Independent Researcher London, UK

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Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care

Prabhjot Singh, Abhishek Gupta, Chris Betz, Abe Flansburg+3

Altitude

Clinical Decision Support Preference Learning RL from Human Feedback

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We reframe clinician overrides of clinical AI recommendations as implicit preference data - the same signal structure exploited by reinforcement learning from human feedback (RLHF), but richer: the annotator is a domain expert, the alternatives carry real consequences, and downstream outcomes are observable. We present a formal framework extending standard preference learning with three contributions: a five-category override taxonomy mapping override types to distinct model update targets; a preference formulation conditioned on patient state s, organizational context c, and clinician capability kappa, where kappa decomposes into execution capability kappa-exec and alignment capability kappa-align; and a dual learning architecture that jointly trains a reward model and a capability model via alternating optimization, preventing a failure mode we term suppression bias-the systematic suppression of correct-but-difficult recommendations when clinician capability falls below the execution threshold. We argue that chronic disease management under outcome-based payment contracts produces override data with uniquely favorable properties-longitudinal density, concentrated decision space, outcome labels, and natural capability variation-and that training environments combining longitudinal outcome measurement with aligned financial incentives are a necessary condition for learning a reward model aligned with patient trajectory rather than with encounter economics. This framework emerged from operational work to improve clinician capability in a live value-based care deployment.

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