1. 8.7 Evaluating Pretrained models for Deployable Lifelong Learning
   • Authors: Kiran Lekkala, Eshan Bhargava, Laurent Itti
   • Reason: Contributes a benchmark for Visual Reinforcement Learning and a novel lifelong learning system which may significantly influence the RL and continual learning research community.
2. 8.4 A density estimation perspective on learning from pairwise human preferences
   • Authors: Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann Dauphin
   • Reason: Centers on generative processes for pairwise preferences in reinforcement learning, with theoretical and empirical backing, authored by renowned researchers from reputable institutions.
3. 8.3 A Joint Gradient and Loss Based Clustered Federated Learning Design
   • Authors: Licheng Lin, Mingzhe Chen, Zhaohui Yang, Yusen Wu, Yuchen Liu
   • Reason: Addresses the challenge of non-IID data in Federated Learning with a novel clustering method that may enhance FL research with potential impact on distributed machine learning systems.
4. 8.1 Federated Transformed Learning for a Circular, Secure, and Tiny AI
   • Authors: Weisi Guo, Schyler Sun, Bin Li, Sam Blakeman
   • Reason: Discusses the overarching themes of circular, secure, and tiny AI, relevant to reinforcement learning for edge devices, presented by authors likely connected to industry applications.
5. 7.9 Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework
   • Authors: Chunjing Gan, Binbin Hu, Bo Huang, Tianyu Zhao, Yingru Lin, Wenliang Zhong, Zhiqiang Zhang, Jun Zhou, Chuan Shi
   • Reason: Offers an innovative approach to understanding fund investment decisions using a disentangled learning framework, which could be influential in the financial technology sector and recommendation systems.
6. 7.9 Scalable AI Safety via Doubly-Efficient Debate
   • Authors: Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras
   • Reason: Proposes debate protocols in AI safety, with potential relevance to reinforcement learning safety, authored by researchers with previous influential work in AI safety.
7. 7.7 Achieving Margin Maximization Exponentially Fast via Progressive Norm Rescaling
   • Authors: Mingze Wang, Zeping Min, Lei Wu
   • Reason: Introduces a novel algorithm that relates to optimization landscapes similar to those found in reinforcement learning, authored by academics with potential cross-application insights.
8. 7.6 Learning Hierarchical Polynomials with Three-Layer Neural Networks
   • Authors: Zihao Wang, Eshaan Nichani, Jason D. Lee
   • Reason: Provides theoretical insights and practical advances in learning hierarchical functions with three-layer neural networks, potentially influencing the theory-led design of deep learning architectures.
9. 7.5 Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies from MPC using Tube-Guided Data Augmentation and NeRFs
   • Authors: Andrea Tagliabue, Jonathan P. How
   • Reason: Combines imitation learning with MPC for reinforcement learning, demonstrating practical efficiency improvements, authored by researchers with a track record in robotics and AI.
10. 7.2 Can Physics Informed Neural Operators Self Improve?
    • Authors: Ritam Majumdar, Amey Varhade, Shirish Karande, Lovekesh Vig
    • Reason: Introduces a self-training method to bridge the performance gap between data-driven and physics-informed neural operators, potentially catalyzing a new direction of research in physics-aware machine learning.