RL from Human Feedback

RL: Reinforcement Learning

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A Scoping Review and Experimental Study on Reinforcement Learning from Human Feedback for Human-Robot Collaboration
Oct 7, 2026Alexandra Coroiu, Andrea Vogt, Viktor Werbilo +3RL from Human Feedback Human-Robot Collaboration
DLR Institute for AI Safety and Security, Wilhelm-Runge-Straße 10, 89081, Ulm, Germany · DLR Institute for AI Safety and Security, Rathausallee 12, 53757, Sankt Augustin, Germany
Constraint Tree Exploration for Learning from Language Feedback
Oct 6, 2026Shaoang Li, Daniel R. Jiang, Jian LiRL Exploration RL from Human Feedback
Stony Brook University · Meta Platforms
EnGRICH: Enhancing Generative Reward Modeling with Critiques from Humans
Oct 4, 2026Xuancheng Li, Beining Wang, Haitao Li +7Reward Modeling Process Reward Models
Department of Computer Science and Technology, Tsinghua University, Beijing, China · Tencent, Beijing, China
Robust Risk-Sensitive Reinforcement Learning from Corrupted Human Feedback
Sep 30, 2026Xinyi Ni, Lifeng LaiRisk-Sensitive RL Robust RL
Department of Electrical and Computer Engineering, University of California, Davis, CA 95616, USA
ReF-HIL: Shaping the Critic around Human Action Neighborhoods for Efficient Human-in-the-Loop Reinforcement Learning
Sep 29, 2026Shaoyin Luo, Song Wang, Shibo Xia +5RL for Robotics Reward Shaping
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Diffusion Reward Models
Sep 27, 2026Xiangyang Wang, Bingxiang He, Zeyuan Liu +13Reward Modeling Multimodal Reward Modeling
Tsinghua University · University of Illinois Urbana-Champaign
Distortion of AI Alignment Revisited: RLHF is a Decent Utilitarian Aligner
Sep 14, 2026Kazusato Oko, Annie Ulichney, Nika Haghtalab +1LLM Alignment Preference Alignment
Subspace Inference Enables Efficient Active Reward Learning from Preferences
Sep 3, 2026Yutai Zhou, Erdem BıyıkBayesian RL Preference Learning
Thomas Lord Department of Computer Science University of Southern California, Los Angeles, USA
User Feedback Provides a Unique Signal that LLMs Can not Detect
Sep 2, 2026Shachar Don-Yehiya, Leshem Choshen, Omri AbendLLM Evaluation RL from Human Feedback
The Hebrew University of Jerusalem · IBM Research · MIT +1
From Answers to Policies: Efficient In-Context Learning System through Emulating Expert Investigation
Aug 17, 2026Minh-Ha Nguyen, Ngoc-Ngo Quang Tran, Thuy Dung Nguyen +1In-Context Learning In-Context RL
Department of Epidemiology, Vanderbilt University, Nashville, TN, USA · Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA · Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA +1
Learning from Online User Feedback for Shopping Agents
Aug 12, 2026Haobo Zhang, Kelong Mao, Sulong Xu +2Large Language Model-Based Recommendation User Preference Modeling
Gaoling School of Artificial Intelligence, Renmin University of China
Procedural Fairness Failures in RLHF from Preference Averaging
Aug 10, 2026M P V S Gopinadh, Karthik Kamuju, Kummari Avinash +2Reward Modeling Algorithmic Fairness
Vishnu Institute of Technology
LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback
Jul 31, 2026Manith Adikari, Bei Peng, Samuele Vinanzi +1Multi-Objective Reinforcement Learning Reinforcement Learning
Department of Computer Science, University of Manchester · Centre for Robotics & AI, University of Manchester · School of Computer Science, University of Sheffield +1
Meta-Learned Reward Shaping for Reinforcement Learning from Human Feedback
Jul 28, 2026Yunpeng ChuLLM Alignment Reward Shaping
Stony Brook University
An offline approach to fNIRS-guided reinforcement learning for robot behavior
Jul 15, 2026Julia Santaniello, Madelaine Brower, Benson Jiang +3Reinforcement Learning Robot Learning
Tufts University, School of Engineering
RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences
Jul 15, 2026Logan Mondal Bhamidipaty, Mykel Kochenderfer, Subramanian RamamoorthyWorld Model Learning Offline RL
University of Edinburgh · 2Stanford University
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
Jul 9, 2026Ali Larian, Qian Lin, Chang Zong Wu +1Reinforcement Learning Human-in-the-Loop AI
Kahlert School of Computing, University of Utah.
Attention Limited Reward Learning
Jul 6, 2026Wenqian XingPairwise Preference Learning Reward Modeling
Management Science and Engineering Department, Stanford University
Human-Centric Reflective Architecture for Human-AI Collaborative Decision-Making
Jul 3, 2026Andreas Kouridakis, Dimitrios Patiniotis Spyropoulos, George VourosHuman Preference Modeling Human-in-the-Loop AI
University of Piraeus Piraeus, Greece
PS-PPO: Prefix-Sampling PPO for Critic-Free RLHF
Jun 29, 2026Doo Hwan Hwang, Kee-Eung KimReinforcement Learning RL for Language Model Reasoning
Kim Jaechul Graduate School of AI, KAIST, Daejeon, South Korea.
Qwen-Image-2.0-RL Technical Report
Jun 25, 2026Yixian Xu, Kaiyuan Gao, Yuxiang Chen +25Reward Modeling RL for Diffusion Models
Equal contribution. · Corresponding author.
Retroactive Advantage Correction: Closed-Form V-Trace Bias Correction for Delay-Aware RLHF
Jun 25, 2026Arnav RajAsynchronous RL Reinforcement Learning
Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India.
PEBS: Per-rater Empirical-Bayes Shrinkage for RLHF Reward-Model Calibration
Jun 25, 2026Arnav RajReward Modeling Post-Hoc Calibration
Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India.
Sculpting NeRF Geometry: Human-Preference Fine-Tuning of a 3D-Aware Face GAN
Jun 25, 2026Archer Moore, Mingming Gong, Liam Hodgkinson3D Shape Generation RL for Generative Models
School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback
Jun 23, 2026Andreas Chouliaras, Luke Connolly, Dimitris ChatzpoulosReward Modeling Human-in-the-Loop AI
School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland
AI Alignment From Social Choice Perspectives
Jun 19, 2026Daniel Halpern, Evi Micha, Ariel D. Procaccia +3LLM Alignment AI Alignment
Google Research · University of Southern California · Harvard University
Uncertainty-Aware Reward Modeling for Stable RLHF
Jun 18, 2026Licheng Pan, Haocheng Yang, Haoxuan Li +7Reward Modeling Reward Hacking
Zhejiang University · Xiaohongshu Inc · National University of Singapore +1
Generating Natural and Expressive Robot Gestures through Iterative Reinforcement Learning with Human Feedback using LLMs
Jun 17, 2026Chris Lee, Flora Salim, Benjamin Tag +1Language Model-Based Control Human Motion Generation
School of Computer Science and Engineering, University of New South Wales, Sydney, Australia · Universidad Central de Chile, Santiago, Chile
ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning
Jun 15, 2026Wei Xiao, Weiliang Tang, Yuying Ge +4RL for Robotics Robotic Manipulation
1XPENG Robotics · 2Fudan University · 3The Chinese University of Hong Kong +1
Hidden Consensus:Preference-Validity Compression in Human Feedback
Jun 9, 2026Dorcas Chia Ern Chua, Karen Myn Hui Lee, Jia Yue Tan +9Human Preference Modeling Preference Alignment
1YTL AI Labs · 2Universiti Malaya

Oct 7, 2026, cs.HCJ/K move · Enter open · S save

A Scoping Review and Experimental Study on Reinforcement Learning from Human Feedback for Human-Robot Collaboration

Alexandra Coroiu, Andrea Vogt, Viktor Werbilo, Andreas Poppele+2

DLR Institute for AI Safety and Security, Wilhelm-Runge-Straße 10, 89081, Ulm, Germany · DLR Institute for AI Safety and Security, Rathausallee 12, 53757, Sankt Augustin, Germany

RL from Human Feedback Human-Robot Collaboration Robot Safety Human-in-the-Loop Evaluation

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Human-Robot Collaboration (HRC) can facilitate mass customisation in Industry 4.0, with Reinforcement Learning from Human Feedback (RLHF) representing a promising approach for developing safe AI-based robots. Practical challenges remain regarding safety during AI development, human feedback quality, and bidirectional human-robot adaptation. We conducted a scoping review of RLHF in HRC systems, mapping methods that address these challenges. Following PRISMA guidelines, we screened 199 records and included 20 peer-reviewed publications (2020-2025) spanning multiple HRC domains. To our knowledge, this is the first review focused on the bidirectional, closed-loop design of RLHF. Our review found multiple feedback modalities enabling data collection in various feedback formats. Collected data can be integrated at different stages of AI training, resulting in a multi-step development process. Pilot experiments are commonly used to evaluate HRC systems based on both human and robot metrics. To empirically test a key gap identified in the review, we conducted a between-subjects VR experiment comparing system- and user-initiated feedback on robot proxemic behaviour for safe navigation. Using Bayesian models, we analysed the relation between the collected feedback and safety metrics: psychological safety (post-experiment questionnaire) and physical safety (inverse time-to-collision). Results show that user-initiated feedback captures perceived safety better than system-initiated feedback, indicating that feedback timing directly affects feedback quality. Our review and experiment findings show that RLHF relies on appropriate feedback methods to ensure AI safety in HRC, and future RLHF research should prioritise realistic HRC experiments evaluating the effects of feedback collection methods on relevant human and robot metrics.

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