arXiv:2606.03131v1 Announce Type: new Abstract: Reward models are central to large language model (LLM) alignment, but they remain vulnerable to reward hacking. To evaluate reward-model robustness, we introduce RewardHackBench containing 13 reward-hacking patterns covering real life high-stakes domains and general settings, and we find severe failures on specific subcategories across eight reward models. To mitigate these failures, we propose HARVE, a training-free reward-head editing method for scalar reward models. Instead of fine-tuning the reward model, HARVE identifies a multi-directional
The rapid advancement and deployment of large language models are exposing critical vulnerabilities in their underlying reward models, driving urgent research into robustness and security.
Reward models are foundational for aligning AI with human values; vulnerabilities like hacking could lead to unpredictable and potentially harmful AI behaviors, undermining trust and safety in advanced LLMs.
The development of methods like HARVE offers a pathway to more resilient and trustworthy AI alignment mechanisms, shifting focus from pure performance to security and robustness in reward model design.
- · AI developers
- · AI safety researchers
- · Users of aligned LLMs
- · Malicious actors attempting to hack reward models
- · AI systems vulnerable to reward hacking
Reward models become more robust against adversarial attacks, improving the safety profile of LLMs.
Increased adoption of secure reward model architectures, leading to higher enterprise and public trust in AI applications.
The development of a new sub-discipline focused on AI 'immunology' or 'cybersecurity' specifically for reward and alignment mechanisms, similar to traditional software security.
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Read at arXiv cs.LG