Reward-Robust RLHF in LLMs
Keywords: RLHF, LLM, robustness, alignment
TL;DR: We introduce a reward-robust RLHF framework for LLMs aimed at mitigating issues raised by imperfection of RMs.
Abstract: As Large Language Models continue to progress toward more advanced forms of intelligence, Reinforcement Learning from Human Feedback is increasingly seen as a key pathway toward achieving Artificial General Intelligence. However, the reliance on reward-model-based alignment methods introduces significant challenges due to the inherent instability and imperfections of Reward Models (RMs), which can lead to critical issues such as reward hacking and misalignment with human intentions. In this paper, we introduce a reward-robust RLHF framework aimed at addressing these fundamental challenges, paving the way for more reliable and resilient learning in LLMs. Our approach introduces a novel optimization objective that carefully balances performance and robustness by incorporating Bayesian Reward Model Ensembles to model the uncertainty set of reward functions. This allows the framework to integrate both nominal performance and minimum reward signals, ensuring more stable learning even with imperfect RMs. Empirical results demonstrate that our framework consistently outperforms baselines across diverse benchmarks, showing improved accuracy and long-term stability. We also provide a theoretical analysis, demonstrating that reward-robust RLHF approaches the stability of constant reward settings, which proves to be acceptable even in a stochastic-case analysis. Together, these contributions highlight the framework’s potential to enhance both the performance and stability of LLM alignment.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 761
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