Gradient Regularization Mitigates Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards
arXiv:2602.18037v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) or Verifiable Rewards (RLVR) are two key steps in the post-training of modern Language Models (LMs). A common problem is reward hacking, where the policy may exploit inaccuracies of the reward and learn an unintended behavior. Most previous works address this by limiting the policy update with a Kullback-Leibler (KL) penalty towards a reference model. We propose a different framing: Train the LM in a way that biases policy updates towards regions in which the reward is more accurate. Fir
The rapid advancement and deployment of large language models are exposing critical weaknesses like reward hacking in RLHF, necessitating more robust alignment techniques.
Reward hacking undermines the reliability and safety of advanced AI systems, and solutions to this problem are crucial for their broader adoption and trustworthy operation.
The focus for AI alignment is shifting towards gradient regularization and intrinsic reward modeling as alternatives or complements to traditional KL penalties, potentially leading to more stable and predictable AI behaviors.
- · AI safety researchers
- · Developers of robust LLMs
- · AI platform providers
- · Developers relying solely on simple RLHF
- · Applications susceptible to adversarial AI behaviors
More reliable and less exploitable AI models will emerge, improving trust in deployed systems.
This could accelerate the integration of AI into sensitive applications requiring high assurance and predictable outcomes.
Reduced reward hacking risks might mitigate some regulatory concerns around AI control and safety, influencing policy development.
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Read at arXiv cs.AI