arXiv:2602.02572v2 Announce Type: replace Abstract: Existing alignment methods directly use the reward model learned from user preference data to optimize an LLM policy, subject to KL regularization with respect to the base policy. This practice is suboptimal for maximizing user's utility because the KL regularization may cause the LLM to inherit the bias in the base policy that conflicts with user preferences. While amplifying rewards for preferred outputs can mitigate this bias, it also increases the risk of reward hacking. This tradeoff motivates the problem of optimally designing reward mo
The rapid advancement and deployment of large language models (LLMs) necessitate more sophisticated alignment techniques to maximize user utility and mitigate inherent biases.
This research addresses a core challenge in AI development by proposing a novel theoretical framework to optimize reward shaping, which is critical for making AI systems more reliable and trustworthy.
The proposed Stackelberg game perspective offers a new way to design reward models for LLMs, potentially leading to more aligned and less biased AI outputs compared to current methods.
- · AI researchers
- · LLM developers
- · Users of AI systems
- · AI ethics and safety organizations
- · Developers relying solely on current suboptimal alignment techniques
- · AI systems prone to reward hacking
Improved alignment for large language models, reducing unintended biases and increasing user satisfaction.
Faster adoption of AI agents and applications across industries due to enhanced reliability and trustworthiness.
Increased public trust in AI systems leading to broader societal integration, possibly influencing regulatory frameworks for AI development.
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Read at arXiv cs.LG