Mitigating Reward Hacking in RLHF via Advantage Sign Robustness
Fuente:
arXiv
Saved in:
| Main Authors: | Ono, Shinnosuke, Ackermann, Johannes, Nishimori, Soichiro, Ishida, Takashi, Sugiyama, Masashi |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Gradient Regularization Prevents Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards
by: Ackermann, Johannes, et al.
Published: (2026)
by: Ackermann, Johannes, et al.
Published: (2026)
Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback
by: Ackermann, Johannes, et al.
Published: (2025)
by: Ackermann, Johannes, et al.
Published: (2025)
Reward Shaping to Mitigate Reward Hacking in RLHF
by: Fu, Jiayi, et al.
Published: (2025)
by: Fu, Jiayi, et al.
Published: (2025)
ODIN: Disentangled Reward Mitigates Hacking in RLHF
by: Chen, Lichang, et al.
Published: (2024)
by: Chen, Lichang, et al.
Published: (2024)
Towards Scalable Oversight via Partitioned Human Supervision
by: Yin, Ren, et al.
Published: (2025)
by: Yin, Ren, et al.
Published: (2025)
Reward-Robust RLHF in LLMs
by: Yan, Yuzi, et al.
Published: (2024)
by: Yan, Yuzi, et al.
Published: (2024)
On Symmetric Losses for Robust Policy Optimization with Noisy Preferences
by: Nishimori, Soichiro, et al.
Published: (2025)
by: Nishimori, Soichiro, et al.
Published: (2025)
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF
by: Zhu, Banghua, et al.
Published: (2024)
by: Zhu, Banghua, et al.
Published: (2024)
InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling
by: Miao, Yuchun, et al.
Published: (2024)
by: Miao, Yuchun, et al.
Published: (2024)
RLHF Workflow: From Reward Modeling to Online RLHF
by: Dong, Hanze, et al.
Published: (2024)
by: Dong, Hanze, et al.
Published: (2024)
Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment
by: Wang, Ye, et al.
Published: (2026)
by: Wang, Ye, et al.
Published: (2026)
Reward Model Overoptimisation in Iterated RLHF
by: Wolf, Lorenz, et al.
Published: (2025)
by: Wolf, Lorenz, et al.
Published: (2025)
How to Evaluate Reward Models for RLHF
by: Frick, Evan, et al.
Published: (2024)
by: Frick, Evan, et al.
Published: (2024)
Quantile Regression for Distributional Reward Models in RLHF
by: Dorka, Nicolai
Published: (2024)
by: Dorka, Nicolai
Published: (2024)
Information-Theoretic Reward Decomposition for Generalizable RLHF
by: Mao, Liyuan, et al.
Published: (2025)
by: Mao, Liyuan, et al.
Published: (2025)
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity
by: Ackermann, Johannes, et al.
Published: (2024)
by: Ackermann, Johannes, et al.
Published: (2024)
Offline Reinforcement Learning with Domain-Unlabeled Data
by: Nishimori, Soichiro, et al.
Published: (2024)
by: Nishimori, Soichiro, et al.
Published: (2024)
RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation
by: Liang, Kaiqu, et al.
Published: (2025)
by: Liang, Kaiqu, et al.
Published: (2025)
It Takes Two: On the Seamlessness between Reward and Policy Model in RLHF
by: Lu, Taiming, et al.
Published: (2024)
by: Lu, Taiming, et al.
Published: (2024)
Feedback Loops With Language Models Drive In-Context Reward Hacking
by: Pan, Alexander, et al.
Published: (2024)
by: Pan, Alexander, et al.
Published: (2024)
Recursive Reward Aggregation
by: Tang, Yuting, et al.
Published: (2025)
by: Tang, Yuting, et al.
Published: (2025)
RLHF in an SFT Way: From Optimal Solution to Reward-Weighted Alignment
by: Du, Yuhao, et al.
Published: (2025)
by: Du, Yuhao, et al.
Published: (2025)
Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX
by: Nishimori, Soichiro, et al.
Published: (2026)
by: Nishimori, Soichiro, et al.
Published: (2026)
Reward Generalization in RLHF: A Topological Perspective
by: Qiu, Tianyi, et al.
Published: (2024)
by: Qiu, Tianyi, et al.
Published: (2024)
Countdown-Code: A Testbed for Studying The Emergence and Generalization of Reward Hacking in RLVR
by: Khalifa, Muhammad, et al.
Published: (2026)
by: Khalifa, Muhammad, et al.
Published: (2026)
CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks
by: Wang, Hao, et al.
Published: (2026)
by: Wang, Hao, et al.
Published: (2026)
Blockwise Advantage Estimation for Multi-Objective RL with Verifiable Rewards
by: Pavlenko, Kirill, et al.
Published: (2026)
by: Pavlenko, Kirill, et al.
Published: (2026)
Calibration Collapse Under Sycophancy Fine-Tuning: How Reward Hacking Breaks Uncertainty Quantification in LLMs
by: Sahoo, Subramanyam
Published: (2026)
by: Sahoo, Subramanyam
Published: (2026)
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework
by: Hu, Jian, et al.
Published: (2024)
by: Hu, Jian, et al.
Published: (2024)
Adaptive Margin RLHF via Preference over Preferences
by: Chittepu, Yaswanth, et al.
Published: (2025)
by: Chittepu, Yaswanth, et al.
Published: (2025)
RLHF and IIA: Perverse Incentives
by: Xu, Wanqiao, et al.
Published: (2023)
by: Xu, Wanqiao, et al.
Published: (2023)
Reward Hacking Mitigation using Verifiable Composite Rewards
by: Tarek, Mirza Farhan Bin, et al.
Published: (2025)
by: Tarek, Mirza Farhan Bin, et al.
Published: (2025)
Repairing Reward Functions with Feedback to Mitigate Reward Hacking
by: Hatgis-Kessell, Stephane, et al.
Published: (2025)
by: Hatgis-Kessell, Stephane, et al.
Published: (2025)
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought
by: Zhang, Zhen-Yu, et al.
Published: (2024)
by: Zhang, Zhen-Yu, et al.
Published: (2024)
Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning
by: Fei, Wu, et al.
Published: (2025)
by: Fei, Wu, et al.
Published: (2025)
Dataset Reset Policy Optimization for RLHF
by: Chang, Jonathan D., et al.
Published: (2024)
by: Chang, Jonathan D., et al.
Published: (2024)
On Teacher Hacking in Language Model Distillation
by: Tiapkin, Daniil, et al.
Published: (2025)
by: Tiapkin, Daniil, et al.
Published: (2025)
Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation
by: Baumann, Joachim, et al.
Published: (2025)
by: Baumann, Joachim, et al.
Published: (2025)
Adversarial Reward Auditing for Active Detection and Mitigation of Reward Hacking
by: Beigi, Mohammad, et al.
Published: (2026)
by: Beigi, Mohammad, et al.
Published: (2026)
Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards
by: Li, Ming, et al.
Published: (2025)
by: Li, Ming, et al.
Published: (2025)
Similar Items
-
Gradient Regularization Prevents Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards
by: Ackermann, Johannes, et al.
Published: (2026) -
Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback
by: Ackermann, Johannes, et al.
Published: (2025) -
Reward Shaping to Mitigate Reward Hacking in RLHF
by: Fu, Jiayi, et al.
Published: (2025) -
ODIN: Disentangled Reward Mitigates Hacking in RLHF
by: Chen, Lichang, et al.
Published: (2024) -
Towards Scalable Oversight via Partitioned Human Supervision
by: Yin, Ren, et al.
Published: (2025)