Summary of Induced Model Matching: How Restricted Models Can Help Larger Ones, by Usama Muneeb and Mesrob I. Ohannessian
Induced Model Matching: How Restricted Models Can Help Larger Onesby Usama Muneeb, Mesrob I. OhannessianFirst…
Summary of The Edge-of-reach Problem in Offline Model-based Reinforcement Learning, by Anya Sims et al.
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learningby Anya Sims, Cong Lu, Jakob Foerster, Yee…
Summary of Refining Minimax Regret For Unsupervised Environment Design, by Michael Beukman et al.
Refining Minimax Regret for Unsupervised Environment Designby Michael Beukman, Samuel Coward, Michael Matthews, Mattie Fellows,…
Summary of A Critical Evaluation Of Ai Feedback For Aligning Large Language Models, by Archit Sharma et al.
A Critical Evaluation of AI Feedback for Aligning Large Language Modelsby Archit Sharma, Sedrick Keh,…
Summary of Revisiting Data Augmentation in Deep Reinforcement Learning, by Jianshu Hu et al.
Revisiting Data Augmentation in Deep Reinforcement Learningby Jianshu Hu, Yunpeng Jiang, Paul WengFirst submitted to…
Summary of Non-orthogonal Age-optimal Information Dissemination in Vehicular Networks: a Meta Multi-objective Reinforcement Learning Approach, by A. A. Habob et al.
Non-orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approachby A. A.…
Summary of When Do Off-policy and On-policy Policy Gradient Methods Align?, by Davide Mambelli et al.
When Do Off-Policy and On-Policy Policy Gradient Methods Align?by Davide Mambelli, Stephan Bongers, Onno Zoeter,…
Summary of All Language Models Large and Small, by Zhixun Chen et al.
All Language Models Large and Smallby Zhixun Chen, Yali Du, David MguniFirst submitted to arxiv…
Summary of Stochastic Approximation with Delayed Updates: Finite-time Rates Under Markovian Sampling, by Arman Adibi et al.
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Samplingby Arman Adibi, Nicolo Dal Fabbro,…
Summary of Easy As Abcs: Unifying Boltzmann Q-learning and Counterfactual Regret Minimization, by Luca D’amico-wong et al.
Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimizationby Luca D'Amico-Wong, Hugh Zhang, Marc…