1. 9.9 Continual Learning as Computationally Constrained Reinforcement Learning
   • Authors: Saurabh Kumar, Henrik Marklund, Ashish Rao, Yifan Zhu, Hong Jun Jeon, Yueyang Liu, Benjamin Van Roy
   • Reason: The authors are known for their seminal contributions in artificial intelligence and this paper’s novel theoretical framework is geared towards advancing the capabilities of AI, which could have broad implications in multiple domains.
2. 9.7 RLTF: Reinforcement Learning from Unit Test Feedback
   • Authors: Jiate Liu, Yiqin Zhu, Kaiwen Xiao, Qiang Fu, Xiao Han, Wei Yang, Deheng Ye
   • Reason: The paper proposes a novel online RL framework that could significantly improve the performance of large language models for code, a critical area with a significant impact in tech industry.
3. 9.5 Investigating the Edge of Stability Phenomenon in Reinforcement Learning
   • Authors: Rares Iordan, Marc Peter Deisenroth, Mihaela Rosca
   • Reason: The paper explores the highly important topic of stability in reinforcement learning, which can greatly improve reliability of RL algorithms. The authors’ academic reputation also contributes to the paper’s influence.
4. 9.3 Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data
   • Authors: Ruiqi Zhang, Andrea Zanette
   • Reason: This paper offers an algorithm for reinforcement learning with provable guarantees, integrating an offline dataset for policy development. The authors’ novel approach and comprehensive analysis could greatly impact the field.
5. 9.2 Towards Assumption-free Bias Mitigation
   • Authors: Chia-Yuan Chang, Yu-Neng Chuang, Kwei-Herng Lai, Xiaotian Han, Xia Hu, Na Zou
   • Reason: The authors of this paper propose an assumption-free framework for bias mitigation in machine learning. The aim is to increase fairness by automating the identification of biased feature interactions. Due to the critical importance of fairness in machine learning and the authors’ novel approach, this paper is potentially influential.
6. 9.1 Self Expanding Neural Networks
   • Authors: Rupert Mitchell, Martin Mundt, Kristian Kersting
   • Reason: The novel approach proposed here could radically alter the way industrial AI systems are trained and designed, improving system capabilities and potentially reducing training costs.
7. 9.0 When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment
   • Authors: Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon
   • Reason: This paper explores the use of transformers in reinforcement learning, particularly their effectiveness in learning memory and performing credit assignments. Given the popularity and success of transformers in machine learning, this paper’s insights could have substantial influence.
8. 8.9 Efficient Model-Free Exploration in Low-Rank MDPs
   • Authors: Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin
   • Reason: The authors propose a novel sample-efficient and computationally efficient algorithm for exploration in Low-Rank Markov Decision Processes. This potentially influential paper could have broad applications in reinforcement learning.
9. 8.8 Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks
   • Authors: Ritesh Ojha, Wenbo Chen, Hanyu Zhang, Reem Khir, Alan Erera, Pascal Van Hentenryck
   • Reason: This paper addresses the significant challenge of dynamic load planning in service network design. The proposed solution could have a broad impact on efficiency and optimization in the transportation industry.
10. 8.5 Incorporating Deep Q – Network with Multiclass Classification Algorithms
    • Authors: Noopur Zambare, Ravindranath Sawane
    • Abstract: The authors of this paper explore the potential of combining DQN (Deep Q-Network) with multiclass classification algorithms, with a focus on increasing the accuracy of such classifications. The outcomes of this study could be influential in a variety of fields using multiclass classification.