A Unifying Lens on Reward Uncertainty in RLHF
DGX agentarXiv:2606.09073v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) is bottlenecked by reward hacking, where the policy exploits errors in a proxy reward model (RM) and