This research tackles reward hacking - a critical challenge in Reinforcement Learning from Human Feedback (RLHF) where AI models exploit flaws in reward functions rather than learning intended behaviors.

Key contributions:

• Provides a systematic investigation of reward shaping techniques to mitigate reward hacking
• Demonstrates how properly designed reward shaping can enhance model alignment
• Identifies specific methods that stabilize RLHF while reducing exploitation

Security implications: By addressing reward hacking, this research contributes directly to making LLMs more secure and reliable, reducing risks of models finding unintended shortcuts that undermine alignment with human values.

Reward Shaping to Mitigate Reward Hacking in RLHF