arXiv AI July 13, 2026 By Tanmay Ambadkar, Sourav Panda, Shreyash Kale, Jonathan Dodge, Abhinav Verma

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization

arXiv:2602. 07764v2 Announce Type: replace-cross Abstract: Multi-objective reinforcement learning (MORL) seeks to train agents capable of balancing conflicting objectives.

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

arXiv AI

Aug 3

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback

arXiv:2607. 29559v1 Announce Type: new Abstract: Reinforcement Learning (RL) systems are typically trained using a single, well-specified scalar reward function.

By Manith Adikari, Bei Peng, Samuele Vinanzi, Angelo Cangelosi

agents reinforcement-learning benchmarks

arXiv AI

Jun 17

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning

arXiv:2606. 18111v1 Announce Type: cross Abstract: Fairness is an important aspect of decision-making in multi-objective reinforcement learning (MORL), where policies must ensure both optimality and equity across multiple, potentially conflicting objectives.

By Umer Siddique, Peilang Li, Yongcan Cao

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 16

Diversity-Driven Offline Multi-Objective Optimization via Nested Pareto Set Learning

arXiv:2606. 15115v1 Announce Type: new Abstract: Multi-objective optimization (MOO) has emerged as a powerful approach to solving complex optimization problems involving multiple objectives.

By Yiyi Zhu, Yaolin Wen, Xiang Xia, Xin An, Hanyi Si, Xiang Shu, Yangde Fu, Liang Dou, Hong Qian

benchmarks safety

arXiv AI

Aug 5

SMOPD: Multi-Reward Reinforcement Learning via Specialize-and-Merge Online Policy Distillation

arXiv:2608. 03092v1 Announce Type: cross Abstract: We aim to improve model performance in multi-reward reinforcement learning training process.

By Wen Wang, Jiahua Bao, Tu Yongsiqi, Yihao Liu, Haotian Zhou, Haoxuan Ma, Mengyu Zhou, Wenkui Fan, Junwei He, Xiaoxi Jiang, Guanjun Jiang

reinforcement-learning efficiency

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 Machine Learning

Jun 9

Reformulate LLM Reinforcement Learning for Efficient Training under Black-box Discrepancy

arXiv:2606. 08779v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a pivotal post-training paradigm, yet it frequently suffers from unpredictable sub-optimum performance or even training collapses.

By Jiashun Liu, Runze Liu, Xu Wan, Jing Liang, Hongyao Tang, Ling Pan

llms reinforcement-learning

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LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning

Diversity-Driven Offline Multi-Objective Optimization via Nested Pareto Set Learning

SMOPD: Multi-Reward Reinforcement Learning via Specialize-and-Merge Online Policy Distillation

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

Reformulate LLM Reinforcement Learning for Efficient Training under Black-box Discrepancy