Reward Hacking in Reinforcement Learning and RLHF: A Multidisciplinary Examination of Vulnerabilities, Mitigation Strategies, and Alignment Challenges

Reinforcement Learning (RL) agents optimize policies based on provided rewards, yet may exploit unintended loopholes in the reward design, a phenomenon known as reward hacking. With the rise of Reinforcement Learning from Human Feedback (RLHF), this issue has gained new urgency, especially in large language models (LLMs) where subtle misalignments between the reward model and true human intentions can produce outputs that superficially meet criteria while failing to embody authentic correctness or values. Reward hacking is not confined to RL; it also affects domains such as sustainability planning, healthcare analytics, finance, social media sentiment analysis, and computer vision, where imperfect metrics can guide systems astray.This paper provides a comprehensive, multidisciplinary examination of reward hacking vulnerabilities. We elucidate how misaligned metrics can produce superficial success, connect RLHF challenges to analogous issues in other fields, and discuss advanced strategies for mitigating reward hacking. These include formalizing reward hacking definitions, employing interpretable and causal models, embracing multi-objective optimization, human-in-the-loop oversight, and emerging frameworks like decoupled approval mechanisms. Beyond technical solutions, we also consider ethical and societal implications. By integrating insights from multiple sectors, we highlight a path toward more reliably aligned AI systems that genuinely uphold human values.