Summary of A Theoretical Framework For Partially Observed Reward-states in Rlhf, by Chinmaya Kausik et al.
A Theoretical Framework for Partially Observed Reward-States in RLHFby Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano,…
A Theoretical Framework for Partially Observed Reward-States in RLHFby Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano,…
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedbackby Gaurav Pandey, Yatin Nandwani,…
Dense Reward for Free in Reinforcement Learning from Human Feedbackby Alex J. Chan, Hao Sun,…
Improving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensembleby Shun Zhang, Zhenfang Chen,…
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHFby Banghua Zhu, Michael I. Jordan,…
Secrets of RLHF in Large Language Models Part II: Reward Modelingby Binghai Wang, Rui Zheng,…
A Minimaximalist Approach to Reinforcement Learning from Human Feedbackby Gokul Swamy, Christoph Dann, Rahul Kidambi,…
Uncertainty-Penalized Reinforcement Learning from Human Feedback with Diverse Reward LoRA Ensemblesby Yuanzhao Zhai, Han Zhang,…
COPR: Continual Learning Human Preference through Optimal Policy Regularizationby Han Zhang, Lin Gui, Yuanzhao Zhai,…