arXiv:2505.12843v2 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) relies on reward models to align large language models with human preferences. However, RLHF often suffers from reward hacking, wherein policy learning exploits flaws in the trained reward model to maximize reward scores without genuinely aligning with human preferences. A significant example of such reward hacking is length bias, where reward models usually favor longer responses irrespective of actual response quality. Previous works on tackling length bias have notable limitations, these ap
The rapid advancement and deployment of large language models via RLHF are exposing critical vulnerabilities like reward hacking and length bias, necessitating immediate mitigation strategies to improve AI alignment.
Addressing reward model biases is crucial for developing genuinely aligned AI systems, preventing unintended behaviors, and ensuring that advanced LLMs serve human preferences effectively.
New techniques like 'bias fitting' could lead to more robust and reliable reward models, potentially reducing the incidence of 'reward hacking' and improving the quality of AI-generated content.
- · AI developers
- · LLM users
- · AI alignment researchers
- · LLMs with unmitigated biases
- · Bad actors exploiting AI flaws
Improved reward models lead to more human-preferred and less hackable large language models.
Increased trust and adoption of AI systems as they become more reliable and aligned with human values.
Accelerated progress in AGI development as foundational alignment challenges are systematically addressed.
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Read at arXiv cs.LG