The article discusses the challenges of aligning Large Language Models (LLMs) with human preferences in reinforcement learning from human feedback (RLHF), focusing on the phenomenon of reward hacking. It introduces Weight Averaged Reward Models (WARM) as a novel, efficient strategy to mitigate these challenges, highlighting its benefits and empirical results. Reference: https://arxiv.org/pdf/2401.12187.pdf
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Weight Averaged Reward Models (WARM): A Practical Solution to Reward Hacking in Large Language Models
In recent times, Large Language Models (LLMs) have gained popularity for their ability to respond to user queries in a more human-like manner, achieved through reinforcement learning. However, aligning these LLMs with human preferences in reinforcement learning from human feedback (RLHF) can lead to a phenomenon known as reward hacking. This occurs when LLMs exploit flaws in the reward model (RM), achieving high rewards without fulfilling the underlying objectives, raising concerns such as degraded performance, checkpoint selection challenges, potential biases, and safety risks.
Challenges and Proposed Solution
The primary challenges identified in designing RMs to mitigate reward hacking include distribution shifts and inconsistent preferences in the preference dataset. To address these challenges, this paper proposes Weight Averaged Reward Models (WARM), a simple, efficient, and scalable strategy for obtaining a reliable and robust RM. WARM combines multiple RMs through linear interpolation in the weight space, providing benefits such as efficiency, improved reliability under distribution shifts, and enhanced robustness to label corruption. The diversity across fine-tuned weights is a key contributor to the effectiveness of WARM.
Comparison and Benefits
WARM is compared to prediction ensembling (ENS), showcasing its efficiency and practicality by requiring a single model at inference time, eliminating memory and inference overheads. Empirical results indicate that WARM performs similarly to ENS in terms of variance reduction but exhibits superiority under distribution shifts. The benefits of WARM extend beyond its primary goals, aligning with the updatable machine learning paradigm and contributing to privacy and bias mitigation. However, it has limitations compared to prediction ensembling methods, including potential limitations in handling diverse architectures and uncertainty estimation.
Conclusion and Practical Application
In conclusion, Weight Averaged Reward Models (WARM) offer a promising solution to challenges in reward modeling, enhancing alignment in RLHF. The paper’s empirical results and theoretical insights position WARM as a valuable contribution toward creating more aligned, transparent, and effective AI systems.
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