1. 8.9 Principled RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation
   • Authors: Chanwoo Park, Mingyang Liu, Kaiqing Zhang, Asuman Ozdaglar
   • Reason: Addresses the challenge of heterogeneous human feedback in RLHF with novel frameworks; authors are from reputable institutions and the paper presents strong theoretical guarantees.
2. 8.7 MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games
   • Authors: Anran Hu, Junzi Zhang
   • Reason: First fully polynomial RL algorithm for Nash equilibria beyond certain game types; significant for multi-agent RL with theoretical contributions.
3. 8.5 Self-Play Preference Optimization for Language Model Alignment
   • Authors: Yue Wu, Zhiqing Sun, Huizhuo Yuan, Kaixuan Ji, Yiming Yang, Quanquan Gu
   • Reason: Proposes a novel self-play method for language model alignment with theoretical convergence guarantees and state-of-the-art performance in experiments.
4. 8.3 UCB-driven Utility Function Search for Multi-objective Reinforcement Learning
   • Authors: Yucheng Shi, Alexandros Agapitos, David Lynch, Giorgio Cruciata, Hao Wang, Yayu Yao, Aleksandar Milenovic
   • Reason: Introduces an innovative approach to optimising weight vectors in MORL, demonstrating improved performance on benchmark problems.
5. 8.1 No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO
   • Authors: Skander Moalla, Andrea Miele, Razvan Pascanu, Caglar Gulcehre
   • Reason: Offers a critical insight into the dynamics of representation in PPO with empirical studies, leading to a brand-new auxiliary loss that improved PPO agent performance.