arXiv:2602.17658v2 Announce Type: replace Abstract: Reward modeling is central to alignment pipelines such as RLHF, RLAIF, and PPO-based policy optimization, yet its reliability is constrained by limited and heterogeneous human preference data that are expensive to collect at scale. While synthetic augmentation can expand preference supervision, existing methods often augment uniformly or at the representation level, without targeting examples where the reward model is uncertain or prone to mis-ranking. In this paper, we introduce MARS (Margin and Semantic-Aware Data Augmentation for Reward Mo
The paper addresses a critical bottleneck in AI alignment, namely the scalability and reliability of reward modeling, which is foundational for current leading AI policy optimization methods.
Improved reward modeling fidelity could significantly enhance the safety and effectiveness of advanced AI systems, accelerating the development and deployment of more capable AI models.
By introducing margin and semantic-aware data augmentation, MARS offers a novel approach to overcome data scarcity and heterogeneity issues in reward modeling, leading to more robust and aligned AI.
- · AI developers
- · AI safety researchers
- · Companies deploying advanced AI models
- · AI systems prone to misalignment
- · Human data annotators (potential long-term impact on certain tasks)
Reward models become more robust and less reliant on extensive, expensive human preference data.
This leads to faster iteration and deployment of AI models that are better aligned with human intent, reducing certain categories of AI failure modes.
The acceleration of aligned AI development could contribute to breakthroughs in general AI capabilities and their application across various sectors, potentially shifting economic and social structures.
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Read at arXiv cs.LG