RL from Human Feedback

RLHF studies how to use human judgments and interventions to teach AI systems which responses or actions are desirable, especially when goals are hard to specify with a fixed reward. Common approaches learn reward models from pairwise comparisons and optimize policies with PPO or GRPO, while methods such as Direct Preference Optimization learn directly from preference pairs; active learning and online feedback aim to reduce the data required. Research spans language-model alignment and benchmarks such as AlpacaEval and MT-Bench, as well as robot learning from corrections and demonstrations. A central challenge is that feedback can be noisy, biased, or diverse, and optimizing an imperfect reward can cause reward hacking, so work increasingly studies uncertainty, robustness, fairness, and how to measure real-world improvement rather than proxy scores alone.