arXiv Machine Learning July 31, 2026 By Qiyao Ma, Dechen Gao, Rui Cai, Boqi Zhao, Hanchu Zhou, Junshan Zhang, Zhe Zhao

Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

Read the original on arXiv Machine Learning →

arXiv:2604. 07343v2 Announce Type: replace-cross Abstract: Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing diverse human values.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 2

One Bias After Another: Mechanistic Reward Shaping and Persistent Biases in Language Reward Models

arXiv:2603. 03291v2 Announce Type: replace-cross Abstract: Reward Models (RMs) are crucial for online alignment of language models (LMs) with human preferences.

By Daniel Fein, Max Lamparth, Violet Xiang, Mykel J. Kochenderfer, Nick Haber

llms benchmarks safety

arXiv AI

Jun 8

Re-Centering Humans in LLM Personalization

arXiv:2606. 06614v1 Announce Type: cross Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data.

By Lechen Zhang, Jiarui Liu, Tal August

llms

arXiv AI

Jun 18

Steerable Cultural Preference Optimization of Reward Models

arXiv:2606. 18606v1 Announce Type: cross Abstract: It is essential for large language model (LLM) technology to serve many different cultural sub-communities in a manner that is acceptable to each community.

By Minsik Oh, Advit Deepak, Sophie Wu, Douwe Kiela, Ekaterina Shutova

llms reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 25

Bias Fitting to Mitigate Length Bias of Reward Model in RLHF

arXiv:2505. 12843v2 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) relies on reward models to align large language models with human preferences.

By Kangwen Zhao, Jianfeng Cai, Jinhua Zhu, Ruopei Sun, Dongyun Xue, Wengang Zhou, Li Li, Houqiang Li

llms reinforcement-learning safety

arXiv AI

Jun 9

A Regret Minimization Framework on Preference Learning in Large Language Models

arXiv:2606. 09124v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has enabled progress on reasoning-intensive tasks by relying on task-specific verifiers that provide automated correctness signals.

By Suhwan Kim, Taehyun Cho, Geon-Hyeong Kim, Yu Jin Kim, Youngsoo Jang, Moontae Lee, Jungwoo Lee

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 8

AdaJudge: Adaptive Multi-Perspective Judging for Reward Modeling

arXiv:2601. 08097v2 Announce Type: replace-cross Abstract: Reward modeling is essential for aligning large language models with human preferences, yet predominant architectures rely on a static pooling strategy to condense sequences into scalar scores.

By Yongliang Miao, Yangyang Liang, Mengnan Du

llms reinforcement-learning safety

Related stories

One Bias After Another: Mechanistic Reward Shaping and Persistent Biases in Language Reward Models

Re-Centering Humans in LLM Personalization

Steerable Cultural Preference Optimization of Reward Models

Bias Fitting to Mitigate Length Bias of Reward Model in RLHF

A Regret Minimization Framework on Preference Learning in Large Language Models

AdaJudge: Adaptive Multi-Perspective Judging for Reward Modeling