{"total":23,"rows":[{"id":"2209.13085","title":"Defining and Characterizing Reward Hacking","created":"2022-09-27","cited_by_count":203,"area":"alignment"},{"id":"2603.02115","title":"Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons","created":"2026-03-02","cited_by_count":60,"area":"alignment"},{"id":"1911.00459","title":"Positive-Unlabeled Reward Learning","created":"2019-11-01","cited_by_count":37,"area":"alignment"},{"id":"2405.15624","title":"Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning","created":"2024-05-24","cited_by_count":27,"area":"alignment"},{"id":"2201.00012","title":"MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning","created":"2021-12-30","cited_by_count":25,"area":"alignment"},{"id":"2506.15421","title":"Reward Models in Deep Reinforcement Learning: A Survey","created":"2025-06-18","cited_by_count":25,"area":"alignment"},{"id":"2012.06899","title":"Semi-supervised reward learning for offline reinforcement learning","created":"2020-12-12","cited_by_count":23,"area":"alignment"},{"id":"2301.03652","title":"On The Fragility of Learned Reward Functions","created":"2023-01-09","cited_by_count":21,"area":"alignment"},{"id":"2403.12017","title":"Supervised Fine-Tuning as Inverse Reinforcement Learning","created":"2024-03-18","cited_by_count":12,"area":"alignment"},{"id":"2212.00169","title":"Time-Efficient Reward Learning via Visually Assisted Cluster Ranking","created":"2022-11-30","cited_by_count":8,"area":"alignment"},{"id":"2203.13553","title":"Preprocessing Reward Functions for Interpretability","created":"2022-03-25","cited_by_count":7,"area":"alignment"},{"id":"2507.13158","title":"Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities","created":"2025-07-17","cited_by_count":7,"area":"alignment"},{"id":"2608.09853","title":"RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance","created":"2026-08-10","cited_by_count":7,"area":"alignment"},{"id":"2503.13538","title":"From Demonstrations to Rewards: Alignment Without Explicit Human Preferences","created":"2025-03-15","cited_by_count":4,"area":"alignment"},{"id":"2511.17879","title":"Generative Adversarial Post-Training Mitigates Reward Hacking in Live Human-AI Music Interaction","created":"2025-11-22","cited_by_count":3,"area":"alignment"},{"id":"2203.04951","title":"Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method","created":"2022-03-09","cited_by_count":2,"area":"alignment"},{"id":"2407.06459","title":"How Much Progress Did I Make? An Unexplored Human Feedback Signal for Teaching Robots","created":"2024-07-08","cited_by_count":2,"area":"alignment"},{"id":"2402.04856","title":"Explaining Learned Reward Functions with Counterfactual Trajectories","created":"2024-02-07","cited_by_count":1,"area":"alignment"},{"id":"2507.00611","title":"Residual Reward Models: Leveraging Prior Knowledge for Efficient Preference-based Reinforcement Learning in Robotics","created":"2025-07-01","cited_by_count":1,"area":"alignment"},{"id":"2607.08647","title":"Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning","created":"2026-07-09","cited_by_count":1,"area":"alignment"},{"id":"2408.05610","title":"Representation Alignment from Human Feedback for Cross-Embodiment Reward Learning from Mixed-Quality Demonstrations","created":"2024-08-10","cited_by_count":0,"area":"alignment"},{"id":"2510.02180","title":"GRACE: A Language Model Framework for Explainable Inverse Reinforcement Learning","created":"2025-10-02","cited_by_count":0,"area":"alignment"},{"id":"2609.31855","title":"PHIRL: Aligning Learned Rewards with Task Progress for Inverse Reinforcement Learning","created":"2026-09-25","cited_by_count":0,"area":"alignment"}],"next_cursor":null}