arXiv Machine Learning

Adaptive Pluralistic Alignment: A pipeline for dynamic artificial democracy

arXiv:2605. 01642v2 Announce Type: replace Abstract: Prevailing alignment methods target a fixed set of preferences and therefore risk forcing value lock-in as societal norms evolve over time.

arXiv AI

1d ago

Inference-Time Mitigation of Adversarial Political Bias in Large Language Models

arXiv:2608. 14629v1 Announce Type: cross Abstract: As Large Language Models (LLMs) become the mainstay for information retrieval and summarization tasks, ensuring that they are always non-partisan and invulnerable to political bias is a critical step towards safer and more trustworthy Artificial Intelligence (AI).

By Tejaswi V. Panchagnula, Bruce Coburn, Bryce J. Dietrich, Robert X. Browning, Edward J. Delp, Fengqing Zhu

llms nlp reinforcement-learning fine-tuning safety

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

Aug 4

Inference-Time Policy Alignment for Fair Reinforcement Learning

arXiv:2608. 00175v1 Announce Type: new Abstract: Deep reinforcement learning (RL) agents achieve strong performance by optimizing scalar reward functions.

By Umer Siddique, Peilang Li, Conor Wallace, Yongcan Cao

llms agents reinforcement-learning safety

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

arXiv Machine Learning

Jun 29

Democratic ICAI: Debating Our Way to Steering Principles from Preferences

arXiv:2606. 28294v1 Announce Type: new Abstract: Preference-based alignment often struggles to capture the reasoning that underlies human judgments.

By Kevin Kingslin, Anish Natekar, Ashutosh Ranjan, Vivek Srivastava, Savita Bhat, Shirish Karande

llms benchmarks safety

arXiv Machine Learning

Jun 30

REAR: Test-time Preference Realignment through Reward Decomposition

arXiv:2606. 30339v1 Announce Type: cross Abstract: Aligning large language models (LLMs) with diverse user preferences is a critical yet challenging task.

By Fuxiang Zhang, Pengcheng Wang, Chenran Li, Yi-Chen Li, Yuxin Chen, Lang Feng, Chenfeng Xu, Masayoshi Tomizuka, Bo An

llms safety

arXiv AI

Aug 11

Contextual Value Alignment via Multilayer Combinatorial Fusion

arXiv:2608. 07642v1 Announce Type: new Abstract: Aligning large language models (LLMs) with human values remains a major challenge, especially for trustworthy AI.

By Yuanhong Wu, Djallel Bouneffouf, D. Frank Hsu

llms agents reinforcement-learning fine-tuning safety

arXiv AI

Jul 7

Unbiased Alignment for Large Language Models with Noisy Preferences

arXiv:2607. 03248v1 Announce Type: cross Abstract: The alignment of large language models with human preferences is commonly achieved through Reinforcement Learning from Human Feedback or Direct Preference Optimization.

By Jialiang Wang, Xianming Liu, Xiong Zhou, Hui Liu, Haoliang Li

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 8

Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models

arXiv:2505. 10892v2 Announce Type: replace Abstract: Post-training LLMs with RLHF and preference optimization methods (e.

By Akhil Agnihotri, Rahul Jain, Deepak Ramachandran, Zheng Wen

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 11

GrowLoop: Self-Evolving Conversation Evaluation Seeded by Human

arXiv:2605. 28882v2 Announce Type: replace-cross Abstract: With the rapid advancement of large language models, evaluating human-likeness in open-ended conversation has become increasingly important.

By Yihang Lin, Yunze Gao, Zeyang Lin, Dongbo Li, Kun Peng, Yue Liu

llms agents benchmarks safety

arXiv AI

Jun 9

Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective

arXiv:2602. 02572v2 Announce Type: replace-cross Abstract: Existing alignment methods directly use the reward model learned from user preference data to optimize an LLM policy, subject to KL regularization with respect to the base policy.

By Haichuan Wang, Tao Lin, Lingkai Kong, Ce Li, Hezi Jiang, Milind Tambe

llms reinforcement-learning safety

arXiv AI

Jul 23

Rewarding Better Thinking for LLM Preference Alignment

arXiv:2607. 19824v1 Announce Type: new Abstract: LLM preference alignment aims to optimize models toward human preferences across diverse user instructions.

By Xubo Liu, Wenya Guo, Ruxue Yan, Xinying Qian, Ying Zhang

llms reinforcement-learning benchmarks safety

Related stories