The field of reinforcement learning from human feedback (RLHF) is moving towards more robust and aligned methods for training large language models. Recent developments focus on improving the reward modeling process to better capture human preferences and mitigate issues such as reward hacking. Researchers are exploring innovative approaches, including adaptive margin mechanisms, preference-based reward repair, and information-theoretic reward modeling frameworks. These advancements aim to enhance the performance, convergence speed, and generalization capabilities of RLHF models. Notable papers in this area include: APLOT, which introduces an adaptive margin mechanism to improve the robustness of reward models. Repairing Reward Functions with Human Feedback, which proposes a framework for repairing reward functions using human feedback to mitigate reward hacking. Offline and Online KL-Regularized RLHF under Differential Privacy, which investigates the problem of KL-regularized RLHF with local differential privacy. Information-Theoretic Reward Modeling for Stable RLHF, which presents an information-theoretic reward modeling framework to detect and mitigate reward hacking.